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IDEA 11
CI202 IDEA Data Analysis
Workbook
Unlock the Power Within Your Data
CI202
IDEA Data Analysis Workbook
A CaseWare IDEA Document
Copyright © 2020 (v11.2) CaseWare IDEA Inc. All rights reserved.
This manual and the data files are copyrighted with all rights reserved.
No part of this publication may be reproduced, transmitted, transcribed, stored in any retrieval system or translated into any language in any form by any means without the permission of CaseWare IDEA Inc.
CaseWare IDEA Inc. is a privately held software development and marketing company, with offices in Toronto and Ottawa, Canada, related companies in The Netherlands and China, and CaseWare IDEA Partners serving over 90 countries. CaseWare IDEA Inc. is a subsidiary of CaseWare International Inc., the world leader in business-intelligence software for auditors, accountants, and systems and financial professionals.
IDEA is distributed under an exclusive license by:
CaseWare IDEA Inc. 1400 St. Laurent Blvd., Suite 500 Ottawa, ON K1K 4H4 Canada 1-800-265-4332 idea.caseware.com
IDEA® is a registered trademark of CaseWare International Inc.
CI202 IDEA Data Analysis Workbook
Version: CI202_LTR_11.2_01
Contents
Section 1 Introduction to the IDEA Workbook 11
1.1 Setting the Scene 12
1.2 How this Workbook is Organized 13
1.3 Overview of the Use of IDEA 14
1.3.1 Stages of Using IDEA 14
1.3.2 Determine How IDEA is Best Utilized 14
1.4 Audit Objectives 17
1.4.1 Mechanical Accuracy and Valuation 17
1.4.2 Analysis 17
1.4.3 Existence and Validity 17
1.4.4 Completeness 18
1.4.5 Cut-off 18
1.4.6 Other Audit Objectives 18
1.5 Determining Data Requirements 19
1.5.1 Areas to Consider 19
Section 2 Accounts Receivable Audit 21
2.1 Introduction 22
2.1.1 Potential Risks 22
2.1.2 Potential Tests 24
2.2 Case Scenario: Accounts Receivable 26
2.3 Obtaining the Data 27
2.3.1 Requesting Data Files for Audit Purposes 27
2.4 Audit Program 29
2.4.1 Audit Objective 29
Exercise 2A: Audit Setup 30
Exercise 2B: Importing the Accounts Receivable Transactions File 33
Exercise 2C: Selecting a Control Total Field 39
Exercise 2D: Generating Field Statistics 42
Exercise 2E: Reconciling the Database 46
Exercise 2F: Random Record Sampling 54
Exercise 2G: Age Analysis 57
2.5 Understanding the Database Window 64
2.5.1 Data 64
2.5.2 History 64
2.5.3 Field Statistics 64
2.5.4 Results 65
Exercise 2H: Extracting High Value and Old Items 66
Exercise 2I: Identifying and Reviewing All Credit Notes 68
Exercise 2J: Calculating the Net Transaction Amount 71
Exercise 2K: Analyzing the Balances and Taxes by Account 74
Exercise 2L: Checking Debtors Against Authorized Credit Risk 77
2.6 Audit Findings 98
Section 3 Accounts Payable Audit and Fraud Investigation 99
3.1 Introduction 100
3.1.1 Potential Risks 100
3.1.2 Potential Tests 101
3.2 Case Scenario: Accounts Payable 103
3.3 Obtaining the Data 104
3.3.1 Requesting Data Files for Audit Purposes 104
3.4 Audit Program 106
3.4.1 Audit Objective 106
Exercise 3A: Audit Setup 108
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Exercise 3B: Importing the Accounts Payable and Authorized Suppliers Data Files 111
Exercise 3C: Verifying that the Data Files Have Been Correctly Imported 122
Exercise 3D: Identifying Trends, Patterns, Duplicates, and Outliers with Discover 130
Exercise 3E: Analyzing the Profile of Payments 138
Exercise 3F: Identifying High and Unusual Payments 147
Exercise 3G: Identifying Exceptional Transactions 149
3.5 Applying Benford’s Law to Identify Exceptional Items 154
3.6 Data-based Conditions for Benford’s Law 163
3.6.1 Geometrical Series 163
3.6.2 Description of the Same Object 163
3.6.3 Unlimited Data Space (Non-Existence of Minima and Maxima) 163
3.6.4 No Systematic Data Structure 163
Exercise 3H: Test for Duplicate Payments and Records 165
Exercise 3I: Searching for Gaps in the Check Number Sequence 174
Exercise 3J: Searching for Gaps in the Check Date Sequence 179
Exercise 3K: Analyzing Payment Days to Identify Favorable Terms to Suppliers 183
Exercise 3L: Payments to Unauthorized Suppliers 188
Exercise 3M: Analyzing Payments by Supplier 195
3.7 Audit Findings 199
Section 4 Inventory Analysis 201
4.1 Introduction 202
4.1.1 Potential Risks 202
4.1.2 Potential Tests 203
4.2 Case Scenario: Inventory 205
4.3 Obtaining the Data 206
4.3.1 Explanation of the ASCII Delimited File Format 206
4.3.2 Requesting Data Files for Analysis Purposes 207
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4.4 Work Plan 208
Exercise 4A: Project Setup 209
Exercise 4B: Importing the Inventory File 212
Exercise 4C: Verifying that the Database Has Been Correctly Imported 218
Exercise 4D: Identifying Obsolete Inventory Items 221
Exercise 4E: Calculating Usage Ratios and Suggest Obsolescence Provision 226
Exercise 4F: Calculating the Total Provisions for Inventory by Depot 234
Exercise 4G: Test the Accuracy of the Automatic Reordering System 242
Exercise 4H: Analyzing Selling Prices and Margins 246
Exercise 4I: Stratified Random Sampling 255
Exercise 4J: Creating Dashboards with Visualize 259
4.5 Audit Findings 266
Section 5 Common Options 267
5.1 Overview 268
5.2 Designing a Report 269
5.2.1 Modifying the View of the Database 269
5.2.2 Saving the View 273
5.2.3 Resetting the View 274
5.2.4 Opening a View 274
5.2.5 Page Setup 274
5.2.6 Creating a Report 274
5.3 Print Preview and Print the Report 280
5.3.1 Print Preview 280
5.3.2 Printing the Report 281
5.4 Re-Run a Task 283
5.5 Housekeeping 284
5.5.1 Managing Project Folders and Project Properties 284
5.5.2 Backing Up/Restoring Data Files 285
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5.5.3 Using the File Explorer 286
5.5.4 Deleting Data Files 289
5.5.5 Copying Data Files 291
5.5.6 Moving Data Files 291
5.5.7 Renaming Databases 292
5.6 Review and Housekeeping 293
5.6.1 Viewing the History Log of a Database 294
5.6.2 Adding a Comment to the Database 294
5.6.3 Accessing Project Overview 294
5.7 Managing the Use of IDEA 296
Section 6 Other Uses of IDEA 297
6.1 Introduction 298
6.2 Case Scenario: Other Uses of IDEA 299
6.3 Other Potential Tests 300
6.3.1 Sales Transactions 300
6.3.2 Travel Expenses 301
6.3.3 Payroll 302
6.3.4 Access Controls 303
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Preface
Welcome to the IDEA Workbook. IDEA is a comprehensive file interrogation tool for auditors, accountants, and investigators. With IDEA, you can:
l Analyze complex data
l Extract unusual items
l Create audit samples
l Identify duplicate transactions
l Carry out many other tasks that are easy to perform using the specially designed, intuitive Windows interface
This workbook is designed for new IDEA users who are unable to attend a scheduled training course in their area. It is more in-depth than the IDEA Tutorial that is provided with IDEA and illustrates some common audit tests. Step-by-step instructions with solutions are provided to explain how to perform three separate audits/investigations as follows:
l Accounts Receivable Audit
l Accounts Payable Audit and Fraud Investigation
l Inventory Analysis
Each of these audits/investigations increases in complexity in using IDEA’s tasks and tests, building on the experience learned in earlier sections. Therefore, it is recommended that the sections be completed in sequence. However, you may also wish to review a section before conducting a similar audit, investigation, or analysis.
Each section within an audit/investigation has an audit objective as well as an IDEA lesson objective explaining the IDEA features used in the exercise.
The data files for each audit/investigation are provided as an accompanying download. The data files have been created in a variety of file formats to provide experience in importing data from different sources. The numbers of records and fields within each data file have been restricted for speed and practicality.
We hope you enjoy using IDEA.
We wish to acknowledge the assistance provided by Efrim Boritz and Malik Datardina and the University of Waterloo Centre for Information System Assurance (http://accounting.uwaterloo.ca/uwcisa/).
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Section 1
Introduction to the IDEA Workbook
1.1 Setting the Scene For the purposes of this Workbook, you are an audit manager with a firm of Chartered Accountants, Dynamic Accountants, based in Ontario, Canada.
Your client portfolio includes several medium-sized companies, family-owned businesses and retailers. To prosper in the ever-increasing competitive market, you have identified the need to:
l Improve the efficiency of audits
l Improve the quality of audits and business advice given to clients
l Provide added-value services to clients
l Win new clients by offering exciting new services
You have seen demonstrations and read articles on how the IDEA Data Analysis Software will enable you to meet these needs and you have recently purchased IDEA.
You have selected your premiere client, Bright IDEAs Inc., a rapidly expanding retailer of innovative promotional material, to pilot your use of IDEA. Both you and the Chief Financial Officer, Mr. Cuthbert, are concerned that the levels of controls have not increased to meet the growth in the business and after consultation with your client you decide to use IDEA on the Accounts Receivable area of the audit. Additionally, the client asks that you investigate a suspected case of fraud and to assist with a problem they have identifying obsolete stock and the subsequent calculation of inventory provisions. All audits and investigations were highly successful and have identified problems and weaknesses, which would have been extremely difficult to accomplish without the use of IDEA. From your testing and recommendations that the client has implemented, his business has won the "Business of the Year" award and the Chief Financial Officer has publicly praised your services resulting in many new clients.
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1.2 How this Workbook is Organized This Workbook provides an overview of the process of auditing using IDEA and provides step-by-step instructions for carrying out the following audits/investigations that you conduct for Bright IDEAs Inc.:
l Accounts Receivable Audit
l Accounts Payable Audit and Fraud Investigation
l Inventory Analysis
Each audit/investigation is a separate section of the Workbook. An overview to each section explains the objectives of the audit/investigation, how to plan for the testing, and how to request the required data.
A wide range of data file formats are provide with the Workbook via digital download so that you can experience importing different file types.
Instructions are provided for preparing for the exercises, including copying the relevant data files.
The exercises are designed to cover most tests and options within IDEA and both the audit objectives and the exercise details are stated for each test carried out. Each section increases in complexity. Therefore, it is advised that the exercises are completed in order. However, you may wish to repeat or review sections, particularly before conducting a similar live audit. Solutions are provided for each exercise and many images of menu or toolbar button selections, completed dialog boxes, resultant databases, and reports are included for clarity.
You can customize many options, including toolbars, within IDEA. This Workbook provides instructions and images using the default toolbars and settings.
It is recommended to read the following section that outlines the use of IDEA before commencing the exercises.
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1.3 Overview of the Use of IDEA
1.3.1 Stages of Using IDEA
The exact process of using IDEA varies with circumstances, but the main steps are set out in the following chart.
The first three stages are planning stages and are key to getting the most out of IDEA.
1.3.2 Determine How IDEA is Best Utilized
To decide how to use IDEA on an audit, a brief evaluation of the following topics should be completed with an assessment of whether the benefits outweigh the costs:
l Key audit objectives and problems
l Volume and depth of information held on computer
l Ease of downloading data
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Objectives and Problems
IDEA is most useful when there is a problem to solve on an audit. It may be assessing the extent of bad loans, debts, or inventory provisions, looking at performance in collecting cash or simply speeding up the time to prove calculations. Whatever the reason, there should be a core purpose which drives the use and benefits of IDEA.
IDEA is generally used for substantive testing. When there is a need to determine if items are in error, a problem needs to be quantified, or certain items identified, then IDEA should be used. IDEA can also help with checking procedures. An indirect way of checking procedures is to draw an inference about the effectiveness of procedures based on the results of substantive tests; e.g., it may be inferred that if there are no errors then the procedures must be working. Conversely, if there are errors then it may be inferred that procedures are being applied incorrectly and that controls are not effective. IDEA can also be used when checking computer-based controls by re-performing edit checks, matching and other computer-based procedures.
Refer to subsequent sections in this Workbook or the online Help available from the Help menu in IDEA to learn more about the uses of IDEA and its tests.
Volume
Classically, data analysis techniques are appropriate with a large volume of small value transactions. Generally, this is true with IDEA; therefore, the greater the volume of data the more useful IDEA will be for the investigation. If there are fewer than a few hundred records, then IDEA is unlikely to provide much benefit over manual work.
For large files, say over 100,000 records, then a calculation of the required disk size should be made (i.e., number of records multiplied by the approximate space each record takes). IDEA can be used on files with several million records, but a fast computer with a large amount of disk space will be essential.
The amount of data available for each item also makes a difference to the potential benefit of using IDEA. Items with a full history and comprehensive detail will allow a wide variety of tests. IDEA will be restrained to some simple calculations, analysis, and sampling if only the sparest information is available.
Typically, IDEA will be useful when a client has:
l Accounts Receivable: more than 200 balances and 1,000 transactions
l Fixed Assets: more than 1,000 items
l Inventories: more than 1,000 stock lines
l Purchases: more than 2,500 transactions
Ease of Download
If there is a significant cost in using IDEA, then it is likely to be incurred when downloading the data. The data must be transferred from the host (source) computer to the computer
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running IDEA or onto a network location accessible by the auditor/analyst’s computer. This process can be simple and only take a few minutes, or it can be more involved and requires a query or a written report. It may also require the use of equipment to convert data or to read a particular format. If you are using a bureau or agency, they may charge for their services. You may also require some technical assistance that could incur a charge for their services.
In most cases, the client supplies you the accounting data files, a printout of the data, or email messages.
Once a downloading method has been set up and used for a system or computer, subsequent downloads are usually straightforward, and the costs (if any) will be known. However, for the first time, an estimate of time and costs will need to be made.
When to Use IDEA
You can assess the likely benefits of using IDEA from the audit objectives and expected problems, as well as the volume and width of data. Additional consideration should be given to the usefulness of additional analysis over what is currently provided by the system and whether any special factors apply, such as fraud detection and investigation, and special investigations.
Costs are largely determined by the effort involved in obtaining and downloading data. There is some time in performing the IDEA tests, although this is not normally significant. It is also usual that far more "exceptional" items and queries are identified when using IDEA than with other methods, and these may require follow-up time. Alternatively, these items may be given to management for follow-up.
Using IDEA often replaces other tests and provides overall time savings. Alternatively, the reason for using IDEA may be to add to the scope, in which case the extra costs should be defined. You should balance the cost of using IDEA against the benefits.
In summary, use IDEA in audits where:
l There is a core purpose or reason
l There are a reasonable number of records
l There is a depth of information on the items
l The data transfer is technically feasible at a reasonable cost and volumes can be accommodated on computers or networks
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1.4 Audit Objectives In routine audits, use IDEA to meet the following objectives:
1.4.1 Mechanical Accuracy and Valuation
One of the first things you should use IDEA for is to total the records in the file and to prove the accuracy of any calculations. Use the Field Statistics property to add up the records in the file. Calculations can be verified by adding a new field (in the Field Manipulation dialog box), using the Criteria link in the Properties window, or by performing an exception test of miscalculations using one of the Extractions tasks.
The accuracy of any management reports may involve a wider use of tasks to achieve the result of reproducing the report. In these cases, performing a series of joins and/or summarizations or running the Aging task, in the case of aged debt analysis, may be necessary.
1.4.2 Analysis
IDEA can help with the preparation of figures for an analytical review. The Stratification task (on the Analysis tab in the IDEA Ribbon) generates a profile of the population in value bands, groups of codes, or dates. This is particularly useful when auditing assets such as Accounts Receivable, inventories, loans, or for a breakdown of transactions. Additionally, you can summarize the information by codes or sub-codes. Figures can also be compared against previous years to determine trends, and the data can easily be displayed graphically to illustrate trends in a chart.
1.4.3 Existence and Validity
Several tests are available for proving validity:
l Exception testing is used to identify unusual or strange items. These may be simply large items or where the relationship between two pieces of information on an item does not correlate (e.g., rate of pay and pay grade). Many fields of information can also be checked for allowable values (e.g., standard fees).
l Statistical sampling is commonly used to test for validity in a manner that allows for evaluation across a population. The more sophisticated methods, such as Monetary Unit Sampling, are difficult to implement manually. Wherever tests need to refer to physical documentation or assets rather than computer records, an appropriate statistical sampling technique should be used.
l Duplicates testing can be very effective in certain circumstances, such as testing payments or looking for double input of ticket numbers during inventory counts.
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It may be necessary to join (using the Join Databases task) two databases together first to perform a validity test (i.e., transaction file to a master file).
1.4.4 Completeness
The two main tests for completeness are Gap Detection and matches:
l Gap Detection (from the Analysis tab on the IDEA Ribbon) identifies missing numbers or days. There must be a sequential number/date on source documentation for this to work. You can test receivables and sales files for missing invoice numbers. It may also be appropriate to test for gaps on a series of check numbers and completeness of inventory ticket numbers.
l Matching cross-checks between a master file (e.g., maintenance contracts) and transactions (e.g., invoices) and can be performed to see if there are any items on the master file for which a transaction has not been recorded.
1.4.5 Cut-off
Year-end ledger, inventory files, or transaction files can be tested for cut-off by testing for items with dates or sequence numbers above or below the year-end cut-off.
1.4.6 Other Audit Objectives
IDEA can often provide useful information in complex areas such as provisions and valuations by producing several "views" based on a series of different parameters.
Taking inventory as an example, you can:
l Identify which items have not moved in 3, 6, 9, and 12-month bands
l Match items against post year-end sales to see what has not sold
l Test net realizable value on items that have sold
l Check items that were in inventory last year against what items have not sold this year
Any audit, which has a need for analysis of data, computational work, or exception testing, will benefit through using IDEA.
Many of the more interesting uses are fraud related such as cross-matching address files between payroll and accounts payable ledgers, identifying duplicate supplier invoices, and matching bank accounts on payroll with supplier master files.
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1.5 Determining Data Requirements Once it has been determined that IDEA will be used and the objectives have been defined, it is necessary to identify the data required. This is often a two-way process in that the tests to be carried out are limited to the data that is available. Data may be required from more than one file or computer system.
For example, to take inventories, the main master file would be requested. The file might contain quantities, costs, and details of each item. To test for slow-moving items, it may be necessary to request a history file with details of all movements. Similarly, in order to check if any costs are higher than selling price, a separate file of sales prices from the sales system might be required.
There are two main sources of finding out what data is stored:
l IT staff that support the system (possibly third party)
l Users, who input, review and own the data
It is also worthwhile to review the standard reports available as these can be imported as an alternate source for the data.
Systems documentation describes, in theory, what should be held; but in practice, fields may be used for a different purpose or not at all. For example, it is no use designing a test based on date of birth only to find that the users do not bother entering dates of birth! Therefore, a discussion on what data is held is always recommended.
1.5.1 Areas to Consider
In deciding what data is required, it may be worthwhile examining:
l Addresses: These can be used for performing "fuzzy matching", such as checking if any supplier addresses are the same as employee addresses. They may be needed for confirmation purposes. However, addresses take up a considerable amount of space. It may be possible only to include the first portion of an address.
l References: All fields with reference numbers should be picked up. These are likely to be needed for any joins to other files and are good for gap and duplicate tests.
l Flags and codes: It is important to understand what the flags and codes mean. Often additional items are needed based on the codes. They rarely take up much space and should be included.
l Dates: Dates are particularly useful. Care must be taken to understand how dates are used by a system and what they represent.
l Descriptions: Long descriptions can be truncated if space is short, but it is often useful to have a short description on the final output.
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The need for other fields depends on the audit tests. If in doubt, include a field but at the end of an audit, as part of the review process, it is worthwhile noting what has been useful and what was not. In subsequent audits, only necessary fields need to be imported.
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Section 2
Accounts Receivable Audit
2.1 Introduction What are the risks associated with Accounts Receivable that could be addressed by computer assisted audit procedures? And what tests can help address those risks? This section summarizes the business and audit risks that arise in the context of Accounts Receivable and the potential tests that can be used to address those risks.
2.1.1 Potential Risks
The following table identifies the key risks, explains the business and audit implication of each risk and the audit objectives that could be addressed by audit tests.
RISK IMPLICATIONS AUDIT OBJECTIVES
The file is incorrectly consolidated or summed.
Items could be omitted, or the listed items may not be included in the totals reported in the financial statements. The Accounts
Receivable could be overstated or understated depending on the direction of the error.
Completeness
Accuracy
Foreign currency transactions are not translated correctly
Management may not be aware of the impact of transactions in foreign currency and may fail to take steps to manage currency risks. The Accounts Receivable could be overstated or understated depending on the direction of error.
Accuracy
Credit is granted to customers that are likely to default.
The business will sell goods to parties from which they will not be able to collect cash. This has potential implications on liquidity and bad debt expenses.
Valuation
Customers are double
Double billing can negatively affect customer satisfaction. Also, revenues and receivables would be overstated.
Existence, Validity, Valuation
Accounts Receivable are invalid or incorrectly stated.
Accounts could be entirely or partly invalid. Partially invalid accounts may be the result of delays in processing transactions or errors in applying credits and payments to accounts.
Fictitious accounts could be due to fraud.
Existence, Validity, Accuracy
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RISK IMPLICATIONS AUDIT OBJECTIVES
Improper allocation of credits and payments.
As mentioned earlier, improper allocation of payments to accounts could affect the aging of the Accounts Receivable and this would affect management’s ability to determine an effective course of action for handling the customer’s account. For example, sales to customers could be blocked or the customer may be sent to a collection agency, even though the customer is current on their accounts. Improper allocation of payments to accounts could also be an indicator of fraud.
Existence, Validity, Accuracy
Accounts Receivable is not properly aged.
If the aging of the Accounts Receivable is not correct, then management may fail to act on overdue accounts in a timely manner and permit sales to poor credit risks. Also, the calculation of the allowance for doubtful accounts and bad debts expense would be affected.
Valuation
A significant percentage of the receivables is concentrated in a few customers.
The business could be exposed to a combination of credit and liquidity risks if these large customers do not pay their debts in a timely fashion. Also, the company may be deemed to be economically dependent on the identified customers, and this may need to be noted in the financial statements.
Presentation
Improper classification of amounts.
If a credit balance is classified as an Accounts Receivable (AR) instead of an Accounts Payable (AP), then it could distort the current ratio which could be part of a debt covenant.
Presentation
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2.1.2 Potential Tests
The following audit tests are suggested when auditing an Accounts Receivable system. However, the exact tests carried out for a client will depend upon the system used and the data available. Common tests include:
Mechanical Accuracy and Valuation
l Total the file. It often pays to separate out debits and credits.
l Revalue foreign debts, if applicable.
l Check transaction totals to the balance on each account.
Analysis
l Profile debtors using a numeric stratification to see the number of large debts and what proportion of value is in the larger items.
l Produce an aged debt analysis. Consider how to deal with unallocated cash and credit notes. IDEA, by default, ages these on their date rather than allocating against the oldest item or any other treatment. It is often worthwhile splitting the file into invoices, unallocated cash, etc. using multiple extractions, and then aging the individual files.
Exception Tests – Existence and Valuation
l Identify old items (i.e., greater than three months old).
l Identify large balances individually or compared to turnover.
l Select accounts for which no movements have been recorded in a set time.
l Report credit balances.
l Identify unmatched cash or credits.
l Compare balances with credit limits and report exceptions (i.e., accounts with balances above their credit limits or accounts with no credit limits etc.).
l Test for items with invoice dates or numbers outside the expected range.
l Identify partial payments of debts.
l Identify invalid transaction types.
l Identify customer addresses that are "care of" or flagged not to be sent out.
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Gaps and Duplicates
l Test for duplicate invoices (both invoice number and customer/value).
l Use duplicate exception testing for less obvious input errors, such as the same vendor ID assigned to two different vendor names, the same vendor name assigned to two different vendor IDs, payment of the same invoice number and amount to two different vendors, etc.
Matching and Comparison Tests
l Compare the balance on an account with its turnover.
l Match the sales transactions to the Customer Master information to identify sales to new or unauthorized customers and those with exceeded credit limits.
l Compare to Accounts Payable for possible contra accounts.
Sampling
l Select samples (random or specific) for functional testing and confirmation (and produce confirmation letters).
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2.2 Case Scenario: Accounts Receivable As part of the year-end audit of Bright IDEAs Inc., the audit manager of Dynamic Accountants has decided to use IDEA on the Accounts Receivable audit to:
l Improve the quality of the audit
l Improve efficiency
l Provide value added services
This section covers the types of tests that can be carried out using IDEA with some practical examples of obtaining the data, importing, and performing tests.
When auditing Accounts Receivable, the main objective is to form an opinion on the validity of the debt. Items of concern are old invoices, unmatched cash, and large balances, particularly where customers are in difficulty. These can all be identified using exception tests.
One of the key tests is to perform a confirmation by choosing the required balances and sending out letters. This requires customer master information such as addresses and credit details (usually held in a master file) as well as the transactions.
Accounts Receivable data is often obtained by requesting statements as a print report file that can be imported into IDEA. Care must be taken to ensure statements have been produced for all accounts by totaling all items and checking that this agrees to the General Ledger control account.
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2.3 Obtaining the Data Having thought about the possible audit tests the audit manager decides the following data files will be needed for the audit:
l Accounts Receivable database containing data at March 31, 2015
l Customer Master File
One of the simplest format data files to import into IDEA is a Microsoft Access file. Another simple way to import data is to obtain printouts that are "printed" (or spooled) to file rather than the printer and then use the Report Reader tool for IDEA to read the required data into IDEA for testing.
Further discussions with the IT staff have determined that the Accounts Receivable data can be provided in Microsoft Access format.
A suitable printout of the data required from the Customer database exists; therefore, this will be requested in electronic format (i.e., a print report file).
2.3.1 Requesting Data Files for Audit Purposes
Supply the following data from the Accounts Receivable system in Access format.
Outstanding Transactions Between January 1, 2015 and March 31 2015.
FIELD FORMAT
Account Number C 10
Invoice Number C 10
Transaction type C 1
Gross amount N 10,2
Goods & Services Tax – GST N 10,2
Provincial Sales Tax – PST N 10,2
Indicator if paid C 1
Date paid D YYYYMMDD
Customer Reference C 12
In the record definitions, C refers to Character, N to Numeric and D to Date. C 10 means a character field of length 10. N 10, 2 denotes a 10-digit number with 2 decimal places.
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Please supply the following Customer Master data as available on report AR-1 as an electronic report file:
FIELD FORMAT
Account Number C 10
Customer Name C 30
Customer Address 1 C 25
Customer Address 2 C 25
Customer Address 3 C 25
Zip Code C 8
Credit Limit N 8,2
Data is required for the following period: Year-end March 31, 2015.
Supply the following control totals for reconciliation purposes:
l Outstanding balances at year-end March 31, 2015
l Number of transactions supplied
l Number of customers
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2.4 Audit Program From the available or possible tests, the audit manager decides on the appropriate tests and prepares the following audit program for you to complete.
2.4.1 Audit Objective
To ensure that Accounts Receivable shown in the Financial Statement are free from material misstatement.
AUDITING PROCEDURES ASSERTIONS WORK DONE BY (INITIALS)
EXTENT OF
TESTING
1 Obtain the data files from the client and load onto the PC.
Load IDEA and create a Managed project for Accounts Receivable for Bright IDEAs Inc.
2 Verify Accounts Receivable totals agree:
a) Import the Accounts Receivable details,
b) Check the total,
c) Compute field statistics, and
d) Reconcile control totals to the trial balance.
Completeness
Completeness
Completeness, Valuation
3 Select a sample for detailed testing and confirmation. Existence, Ownership
4 Prepare an age accounts analysis to review the profile of debtors
Valuation
5 Identify old and large accounts for detailed testing. Existence, Valuation
6 Identify all credit notes Valuation
7 Prove calculation:
a) Calculations of net value and b) Totals by account
Valuation
8 Identify accounts exceeding their credit limit:
a) Import the Customer Master File and
b) Extract balances over their approved limit.
Valuation
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Exercise 2A: Audit Setup
Objective:
To be able to load data, run IDEA and create a new Managed project.
Exercise Description:
This exercise covers copying data into a folder, loading IDEA and creating a new Managed project.
IDEA Functionality Covered:
l Create a new Managed project
l Specify the project properties
Required Data Files:
The following data files are provided with this workbook:
l Acc_rec2015.accdb - Accounts Receivable at March 31, 2015
l Customer.txt - Customers Report in Text Format
Accessing IDEA
1. From the Windows Start menu (e.g., Windows 10), navigate to and expand the IDEA folder.
2. Click IDEA.
Creating the Accounts Receivable Audit Project
To facilitate housekeeping, it is recommended that a separate project be used for each audit or investigation. All information relating to the audit, including data files, equations, views or report definitions, import definitions, etc. may be stored in the project.
This exercise will explain how to create a project and enter client information that will be printed on all reports. Once a project is set, it remains the active project until changed.
IDEA uses Projects and a Library to store the files created by IDEA. The Projects concept within IDEA allows for better organization of files.
Exercise
Section 2: Accounts Receivable Audit
There are two types of projects you can create:
l Managed projects are stored in the following location on your computer:
C:\Users\[UserID]\Documents\My IDEA Documents\IDEA Projects
l External projects can be stored at other locations on your computer.
After creating a project, you will see that IDEA created the following project structure:
1. From the IDEA Ribbon, make sure the Home tab has been selected and then click New.
To create a new project in pre-IDEA 11.2 versions, on the Home tab, in the Projects group, click Create.
2. Select the Managed project option and enter Accounts Receivable Audit as the Project name.
3. Once you click on OK, IDEA will create a new folder called:
C:\Users\[UserID]\Documents\My IDEA Documents\IDEA Projects\Accounts Receivable Audit
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4. On the Home tab, in the Projects group, click Properties to change the project properties.
5. In the Project Properties dialog box, enter the following information:
l Report name: Accounts Receivable Audit
l Report period: Jan 1, 2015 - Mar 31, 2015
6. Click on OK to accept the changes.
Loading Data
The following data files are provided with the workbook and are required for the Accounts Receivable Audit project.
l Acc_rec2015.accdb - Accounts Receivable at March 31, 2015
l Customer.txt - Customers Report in Text Format
Use either of the following methods to add the required data files to the project:
l Use IDEA to add the data files to the project:
1. In IDEA, click the Library tab.
2. From the Current Project Library, right-click Source Files and click Add File....
3. Navigate to and select the required files.
l Use Windows Explorer to manually copy the data files to the project:
C:\Users\[UserID]\My IDEA Documents\IDEA Projects\Accounts Receivable Audit\Source Files.ILB
This is the default location within a project to store any source files.
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Exercise 2B: Importing the Accounts Receivable Transactions File
Objective:
To import the data file for testing.
Exercise Description:
You will use the Import Assistant to import the Accounts Receivable Transactions file, Acc_rec2015.accdb, into IDEA. Once imported, the file is examined and totaled.
IDEA Functionality Covered:
l Import a Microsoft Access database
l Use the Database window
To determine the number of records in the database:
1. On the Home tab, in the Source Data group, click Import.
To access the Import Assistant in pre-IDEA 11.2 versions, on the Home tab, in the Import group, click Desktop.
Exercise
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The Import Assistant appears.
2. Select Microsoft Access from the list of available formats.
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3. Select the file to import by clicking the Browse button.
By default, you are directed to the Source Files.ILB folder in the active project:
C:\Users\[UserID]\Documents\My IDEA Documents\IDEA Projects\Accounts Receivable Audit\Source Files.ILB
4. Select Acc_rec2015.accdb and click Open.
5. Click Next.
The Microsoft Access dialog box appears. IDEA automatically selects the first table to be imported.
6. Select the Scan records for field length option.
For this exercise, select the Scan records for field length option. You can also select the Scan only option that allows you to specify the number of records to be scanned.
7. Do not select the Create a record number field option for this exercise.
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8. In the Output file name field, enter Accounts Receivable.
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9. Click OK.
The Accounts Receivable - Acc_rec2015 database will be imported, opened, and selected as the active database. When the database is created, the table name is added as a suffix to the output file name.
About the Microsoft Access dialog box:
l Tables - IDEA can import multiple tables from the Microsoft Access file. To import multiple tables, select the tables to be imported by clicking the associated check boxes. To select or deselect all tables, click Include All or Clear All, respectively. When selecting multiple tables, the options selected are applied to all selected tables. Each table is imported as a separate database. In the creation of the tables in Microsoft Access, if the field type is Date/Time, IDEA will create a separate field for the Time. To avoid this, change the field type in Access Table Design to Text. Once in IDEA, the field type can be changed back to a Date field. Also, the Time field can be deleted through Field Manipulation.
l Output File Name - The total length of the full path name which includes the output file name, cannot be greater than 256 characters. The output file name cannot include any of the following characters: / \ < > * " ? [ ] | :
We want to change the database name to Accounts Receivable. To do this we must first close the database.
10. On the File tab, from the Database page, click Close Database.
11. From the File Explorer window, right-click on the database name and click Rename.
The database name is highlighted.
12. Delete "- Acc_rec2015" from the database name.
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13. Press Enter.
The new database name is displayed in the File Explorer window.
14. In the File Explorer window, double-click Accounts Receivable.
The database is now open and selected as the active database in the Database window.
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Exercise 2C: Selecting a Control Total Field
Objective:
To prove the data has been imported correctly.
Exercise Description:
By examining the Accounts Receivable database, you will become familiar with the data and what is on the IDEA screen, as well as learn to specify a Control Total field and determine the number of records in the database.
IDEA Functionality Covered:
Control Total
To select a Control Total field:
1. The Accounts Receivable database appears in the Database window. Maximize the Database window.
Exercise
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2. Scroll through the data using the scroll buttons and bar on the right side of the window until you get to the bottom of the file. Also scroll left and right using the horizontal bar at the bottom of the Database window.
The file contains invoices, credit notes, and cash that are still in the Accounts Receivable database at the year-end. The number of records appears on the Status Bar at the bottom right of the screen (i.e., 300). The available disk space on the project drive will also be displayed on the Status Bar.
3. Scroll back to the top of the file.
When displaying a database, the field (column) widths are optimized to the width of the data or the field names, whichever is wider. Field widths may be adjusted by moving the field name separators or optimized by double-clicking the field name separators.
4. From the Properties window, click Control Total.
5. In the Select Control Total dialog box, click the GROSS_AMT field and click OK.
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6. If prompted to calculate the Control Total, click Yes.
The control total of 435,864.85 appears in the Properties window.
You are now ready to verify that the data has been imported correctly and to commence audit testing.
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Exercise 2D: Generating Field Statistics
Objective:
To verify that the correct data has been supplied and that it has been imported correctly before commencing testing.
Exercise Description:
View the Field Statistics for the Numeric and Date fields in the Accounts Receivable database.
The statistics will be used for:
l Agreeing totals
l Gaining a general understanding of the ranges of values in the database
l Testing for date cut-off, (i.e., the date range)
l Highlighting potential errors/areas of weakness to focus subsequent investigations
IDEA Functionality Covered:
l Field Statistics
l Printing Field Statistics
To generate field statistics for the database:
1. From the Properties window, click Field Statistics.
2. If prompted, click Yes to create statistics.
Statistics are displayed for the GROSS_AMT, GST, and PST fields.
Exercise
Section 2: Accounts Receivable Audit
3. Study the Field Statistics Results for the GROSS_AMT, GST, and PST fields. Note the Net Value, Average Value, Minimum Value, andMaximum Value statistics.
The values displayed in blue are drill down values. Selecting these values allows you to see the items included in that statistics category.
4. In the Field Type box, click Date and study the statistics. Note the Earliest Date and Latest Date statistics, which are useful for determining the period of the database. The totals per client have been provided with the software for reconciliation purposes:
FIELD CONTROL TOTAL PER CLIENT CONTROL TOTAL IN DATABASE
GROSS_AMT 369,255.13 435,864.85
GST 25,193.36 29,511.00
PST 28,525.83 33,408.94
FIELD EARLIEST DATE LATEST DATE
DATE 2014/11/04 2015/04/01
Verify your results from Field Statistics to the above. Can you suggest why the database statistics do not agree with the control totals? The answer is provided in the next exercise.
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5. If your computer is attached to a printer, print the Field Statistics.
a. Select the four fields: GROSS_AMT, GST, PST and DATE.
b. From the Field Statistics toolbar, click the Print button.
c. From the Print dialog box, click OK.
The Field Statistics report for each selected field will be printed two per page.
6. Click Data in the Properties window to return to the database.
Although the Field Statistics may be viewed at any time, once generated, it is recommended that a printout be kept in the audit file along with any reconciliation reports provided.
Your data or the History of your database can be published to a PDF or a Microsoft Word document. Ensure the correct database is active.
7. To publish to a Microsoft Word document:
a. On the File tab, click Export and then click Microsoft Word.
The Windows Save As dialog box appears.
b. Enter the file name.
c. Navigate to the location you want to save the file in.
d. Click Save.
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8. To publish to a PDF file:
a. On the Home tab, in the Export group, click Publish to PDF.
The Publish to PDF dialog box appears.
b. In the File name field, enter the file name and path, or click the Browse button to navigate to the required location and then name the file.
c. Optionally, enter criteria to filter the database for the PDF file.
d. To include database comments, ensure the Include database comments check box is selected.
This option is selected by default. Clear this option to exclude database comments.
e. To include the History, select the Include the History check box.
f. Optionally, in the Sort list, specify the sort order for the database in the output PDF file.
g. To specify the fields for the database in the output PDF file, click Fields. Be default, all fields are included.
h. Click OK.
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Exercise 2E: Reconciling the Database
Objective:
To reconcile the Accounts Receivable database to the totals provided.
Exercise Description:
This exercise will identify some of the reasons why databases do not reconcile to totals provided. It will also explain the following:
l How to perform an extraction
l Rules for entering equations
l How to view and read the history log
l How to annotate the history log
IDEA Functionality Covered:
l Extractions
l Equation Editor
l Database window
l History Logs (View)
l Insert Comments
l Delete Comments
In the previous exercise, we ascertained that the database did not reconcile to the totals provided. This may be due to:
l Errors on importing the database (although this is not generally a problem with Access files)
l Cut-off problems, (i.e., transactions before or after the stated period of the database - after March 31, 2015)
l Or a variety of other reasons
Exercise
Section 2: Accounts Receivable Audit
In fact, there are two problems with this database:
l A post-cutoff date transaction as shown in the Field Statistics.
l Several invoices have been paid (i.e., the PAID_FLAG field contains P). This is a common feature. Paid invoices remain on the system for a period, although marked as paid, so that they can be included on customer statements for information.
It will be necessary to exclude all paid invoices and post-cutoff transactions to reconcile the data to the totals provided. However, the original data file cannot be edited within IDEA, since the integrity of the data is always assured. Therefore, the required data will be extracted into a new database using the Direct Extraction task and entering the following equation:
PAID_FLAG <> "P" .AND. DATE_DATE < "20150401"
This equation identifies unpaid items occurring before April 1, 2015.
Note the following when entering equations:
l When entering text, the text must be enclosed in quotes ("").
l IDEA is case sensitive.
l Dates must be entered in the format YYYYMMDD (without any separating characters such as "/") and enclosed in quotes ("").
l The symbol <> is used for not equal to.
To perform the extraction:
1. On the Analysis tab, in the Extract group, click Direct.
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2. Change the default File Name provided from EXTRACTION1 to Acc Rec March 31 2015.
3. To enter the criteria, click the Equation Editor button .
The Equation Editor window appears and is used to enter the required equation.
4. Enter the required equation as follows:
a. Double-click the field name PAID_FLAG to insert it into the Equation box.
b. Click the <> (not equal to) button.
c. Click the "" button and note that the cursor is inserted between the quotes.
d. Type the letter P (upper case).
e. Move the cursor outside to the right of the quotes.
f. Click the AND button (it inserts .AND.).
g. Double-click the field name DATE_DATE to insert it into the Equation box.
h. Click the < (less than) button.
i. Click the Calendar button and select the date of April 1, 2015 from the calendar. Notice that the date is automatically encapsulated in "" and input in the proper format.
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5. Check the syntax by clicking the Validate button.
6. If a syntax error occurs in your equation, correct the expression and recheck the syntax. The equation should be as shown in the above screen. Click the Validate and Exit button.
7. Click OK to run the extraction. The resultant database Acc Rec March 31 2015 will be displayed.
The Control Total for the GROSS_AMT field, 369,255.13, as displayed in the Properties window now reconciles to the total provided.
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8. View the Field Statistics for this database by clicking Field Statistics in the Properties window. If you get the message, "Statistics are not available for all fields. Do you wish to create statistics for all fields without statistics?" – click Yes. Notice the Net Value totals for the GST and PST fields also reconcile to the totals provided.
9. View the statistics for the DATE field. The Earliest Date is 2014/11/04 and the Latest Date is 2015/03/31.
10. Print and file the Field Statistics reports for each field as explained in the previous exercise.
11. Click Data in the Properties window to return to the database.
12. The Acc Rec March 31 2015 database is now displayed as a "child" of the parent database, Accounts Receivable, in the File Explorer.
History Log
IDEA maintains a hierarchical History log of how each database has been created. These logs cannot be modified and should be printed out at the end of the audit, reviewed and filed along with all other audit documentation. Alternatively, if you use electronic working papers the History log may be exported to a text file.
To view the History log:
1. From the Properties window, click History. The details of the file import, selection of the Control Total field, and the extraction have been recorded. Notice that the History is displayed as a series of collapsed nodes.
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2. Expand each node by clicking on its + button. Alternatively, expand all the nodes by clicking the Expand All Details button on the History toolbar. The IDEAScript code that can be used for re-running the audit is included in the History log.
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Adding a Comment
IDEA allows you to add comments to an active database. Comments are used to explain the findings of a database and can also be used to display warning messages.
1. From the Properties window, in the Comments area, click Add comment to annotate the database.
2. Enter the following comment:
Database Reconciliation – Database reconciles to totals provided by client.
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3. Click the Link column and then click the ellipse button to select Acc Rec March 31 2015.
4. Click OK.
5. Close the Database Comments dialog box.
6. To return to the database, from the Properties window, click Data.
To delete a comment:
Right-click the required comment link in the Comments area of the Properties window and then click Delete Comment.
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Exercise 2F: Random Record Sampling
Objective:
To choose a sample of items for confirmation and determine any other testing to prove validity.
Exercise Description:
This exercise uses random sampling that is the standard sampling technique used by Dynamic Accountants.
IDEA Functionality Covered:
l Extract a random sample of items
l Close an active database
To extract a random sample:
1. Ensure Acc Rec March 31 2015 is the active database with the Data property selected.
2. On the Analysis tab, in the Sample group, click Random.
The Random Record Sampling dialog box appears.
3. Enter 20 as the Number of records to select.
Exercise
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4. Accept the random number seed provided by IDEA. This is used to start the algorithm for calculating the random numbers. If a sample needs to be extended, entering the same random number seed but entering a larger number of records to select will produce the same original records with the required additional records.
5. Accept the default range from which the sample will be selected. By default, the range is the first and last records (i.e., 1 - 253).
6. Leave the Allow Duplicate Records option unselected to prevent IDEA from selecting the same record more than once.
7. In the File name box, enter Sample of Acc Rec Transactions.
8. Click OK to run the sample extraction.
9. View the resultant database. The SAM_RECNO field has been added to the database as the right-most column. This additional field contains the corresponding sampled record numbers from the original database.
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10. On the File tab, click Close Database to close the Sample of Acc Rec Transactions database.
A sample of transactions was extracted in the above exercise. However, you may wish to extract a sample based on account numbers and then extract the transactions relating to the selected account numbers.
1. Summarize the outstanding balances by account number.
2. Extract the required sample of account numbers.
3. Optionally, join the sample of account numbers to the transactions database using the account number fields in each database as the match keys and selecting the Matches only option in the Join Databases task to provide the detail behind each transaction.
This procedure is not used in this exercise, as it requires several additional complex functions within IDEA that will be covered in later exercises.
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Exercise 2G: Age Analysis
Objective:
To produce an age analysis of outstanding invoices at year-end that identifies the number and value of old debts and to make the necessary provisions in the final accounts for potential write-offs.
Exercise Description:
This exercise will teach you how to perform an age analysis, view the Aging Results, understand the Aging Results toolbar options, export the results to a report, and print the report.
IDEA Functionality Covered:
l Aging
l Aging Results view
To perform an Aging analysis:
1. Ensure Acc Rec March 31 2015 is the active database.
2. On the Analysis tab, in the Categorize group, click Aging.
Exercise
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3. Using the calendar control, accessed by clicking on the Calendar button, change the date in the Aging date box (which currently displays the computer’s system date) to 2015/04/01.
The aging date must be entered as one day after the year-end or period- end dates from which you wish to age the data.
4. Do not specify criteria for the test. A criterion would limit the test to only those records satisfying the specified criterion.
5. In the Aging field to use box, accept the DATE_DATE field.
6. In the Amount field to total box, accept GROSS_AMT as the amount field to total for each interval.
7. In the Aging interval in box, accept Days.
8. Change the Aging interval days to: 31, 59, 91, and 121. Only four intervals are required, therefore enter 0 for the 5th and 6th interval.
9. Do not select the Generate detailed aging database option.
10. Do not select the Generate Key summary database option.
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11. In the Create result field, enter Age Analysis Report.
12. Click OK to generate the Results output named Age Analysis Report.
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13. The Age Analysis Report is displayed in a new Results output view of the Database window. The new link to the Results output appears in the Results area of the Properties window.
The Aging Analysis Report produces the following summary information for each age interval:
l # Records: The number of records in a specified interval
l % Records: The percentage of the total number of records
l Debits: The debit value for the records in the specified interval
l % Debits: The percentage of the total debit value
l Credits: The credit value for the records in the specified interval
l % Credits: The percentage of the total credit value
l Net Value: The net value of the records in the specified interval
l % Net Value: The percentage of the total net value
Any credit notes and/ or unallocated cash have been aged on their own date and not offset against oldest items.
A summary record is created in the Aging Analysis Report for each interval specified. Additionally, it produces a summary for:
l Interval Ø: Items dated on or after the aging date, in this case 2015/04/01
l Interval 121+: Items older than the final period specified
l ERR: Items with invalid or missing dates
l Totals: Totals for all intervals
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There are three old debts (i.e., older than 90 days). These will be identified in the following exercise.
14. On the Results toolbar, click on the toggle Graph button. View the graph.
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15. From the Results output toolbar, click the 3D drop-down arrow and click the 3D option to change the graph from 2-dimensional to 3-dimensional.
16. Right-click the graph and then click Add title. Right-click the default title name in the graph and, in the Title field, enter Aging at April 1, 2015.
17. Use the Y Axis list box at the top of the graph to change the y-axis to Net Value.
IDEA 10.3 and later versions automatically calculate the Y-axis scaling compared to previous versions.
18. To save the graph, from the Results toolbar, click the Export button to save the image in a PDF file.
19. Click the View the result grid button to return to the Aging Results output in the Database window.
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20. To return to the database, from the Properties window, click Data.
Only a Results output was generated in this exercise, the additional optional databases were not selected. Please read the IDEA Help system for further information on the optional databases. If the amount of free disk space is a concern (particularly with large databases), careful consideration should be given as to whether optional databases are required.
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2.5 Understanding the DatabaseWindow The Database window is IDEA's main window. Each database is opened into a separate tab in the Database window.
The Database window has several properties that can be accessed through links in the Properties window, including:
l Database
l History
l Field Statistics
l Results
To change properties, click the relevant link in the Properties window. Scroll buttons are available for navigating through the property views.
2.5.1 Data
When a database is opened, the data is displayed in the Data property in the Database window. The Data property has a spreadsheet-like appearance with the record numbers displayed in the first column. Views (i.e., changes to the display format of the data) may be saved and re-opened as required. Changes to the display of data in the Data property are saved with the database and will be re-applied when the database is opened.
2.5.2 History
The History property of the Database window maintains an audit trail or log of all operations carried out on a database, including its import and each audit test. It produces a linear log so that the process of how the file was created can be traced. The History log is contained within the IDEA database (<filename>.imd).
2.5.3 Field Statistics
The Field Statistics property provides statistical information on the values of Numeric, Date, Time, and Character fields within the database, such as net, maximum, minimum, and average values; numbers of debit, credit, and zero value items; and date statistics, such as the earliest and latest date. The Field Statistics property provides a valuable initial analysis of the database by helping the auditor to gain an understanding of the data and perhaps even identify problems to be investigated further, such as negative inventory items, zero-value check payments, or after date transactions.
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2.5.4 Results
The following tasks in IDEA produce Results outputs that are displayed in the Database window
l Gap Detection
l Benford’s Law
l Aging
l Stratification
l Summarization (optional)
l Pivot Table
l Generate Random Numbers
l Chart Data
l Correlation
l Trend Analysis
l Time Series
The Results may be accessed by clicking the relevant link in the Results area in the Properties window. Any number of Results may be created for a database and each will be given an appropriate name.
The Results remain in the IDEA database and may be viewed or printed at any time. The description of the Results tab may be changed to a more meaningful name. Results may also be deleted if required.
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Exercise 2H: Extracting High Value and Old Items
Objective:
To identify high value items and old items.
Exercise Description:
This exercise uses extractions to identify high value invoices (i.e., > $10,000) and old invoices (i.e. before 31 December 2014) found in the Acc Rec March 31 2015 database. This exercise will teach you how to extract data from the database, perform multiple extractions, and understand the benefits of multiple extractions.
IDEA Functionality Covered:
l Extractions
l Multiple Extractions
To perform the extraction:
1. Ensure that Acc Rec March 31 2015 database is selected as the active database and the Data property is selected in the Properties window.
2. On the Analysis tab, in the Extract group, click Direct.
The Direct Extraction dialog box appears.
3. Change the default file name to High Value Amounts.
4. Click the Equation Editor button.
5. Enter the equation GROSS_AMT > 10000.
Do not enter the currency symbol or thousands separators (e.g., commas) when entering numbers.
6. From the toolbar, click the Validate and Exit button to verify the syntax and exit the Equation Editor.
7. Click the second row of the Direct Extraction dialog box and notice that a default file name is provided.
8. Change the default name to Old Invoices.
Exercise
Section 2: Accounts Receivable Audit
9. Click the Equation Editor button.
The Equation Editor appears and is used to enter the required equation.
10. Enter DATE_DATE <= "20141231" using the Calendar button to select the date.
11. From the toolbar, click the Validate and Exit button to verify the syntax and exit the Equation Editor.
12. In the Records to Extract area, accept the default selection of the All option to extract the records from the whole database.
13. Click OK to run the extractions.
14. Two new databases have been created as shown in the File Explorer window. High Value Amounts will be the active database. View this database, record the results, and view the History log. Open the Old Invoices database. View this database, record the results, and view the History log.
l High Value Amounts: There are 10 records totaling $139,479.86.
l Old Invoices: There are 3 records totaling $5,465.29.
15. On the File tab, from the Database page, click Close All Databases to close all databases.
Advantages of Performing Multiple Extractions
The extractions are performed with a single pass through the database, saving considerable time when testing large files. Up to 50 extractions may be carried out at one time.
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Exercise 2I: Identifying and Reviewing All Credit Notes
Objective:
To identify all credit notes.
Exercise Description:
This exercise will identify all credit notes (i.e., where TYPE = "C") using the Criteria property. The differences between the Extraction and Criteria will also be explained.
IDEA Functionality Covered:
Criteria
To identify and review all credit notes:
1. Ensure Acc Rec March 31 2015 is the active database.
2. From the Properties window, click Criteria to access the Equation Editor.
3. Enter the following equation: TYPE = "C".
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4. From the toolbar, click the Validate and Exit button to verify the syntax and exit the Equation Editor.
The criterion will appear beside the Criteria property in the Properties window and will be applied to the database. The Status bar, at the bottom right of the application window, indicates that 34 of the 253 records meet the criterion. The Control Total for the database has changed and is calculated for the 34 records.
5. Right-click the Criteria link in the Properties window and click Clear to return to viewing the whole database.
6. Apply the following new criterion to the database: GROSS_AMT < 0. There are 44 items including 10 unallocated payments (TYPE = "P").
7. Clear the criterion to view all records.
Differences Between Extractions and Criteria
The Criteria property and the Extractions tasks identify items satisfying a given equation. However, the Criteria property does not generate an output database. In the Database window, it displays only those records satisfying the criterion. Additionally, nothing is written to the History log when using the Criteria property.
When running tasks where entering criteria is optional, criteria may be simple or complex. Any criteria used remains available in the Criteria property for that database and can be applied as required. Right-click on Criteria to access the list of Criteria used.
Equations can also be saved and reapplied through the Equation Editor for use in extractions and Virtual fields.
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IDEA lists, and allows you to gain access to, the eight most recently opened Criteria for the current database. When a Criteria is longer than 50 characters, the menu will display only the first 47 characters followed by an ellipsis.
The recent Criteria list is not passed down to child databases.
Conclusion
There are several unallocated payments on account that should be brought to the client’s attention. The number of credits is also high.
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Exercise 2J: Calculating the Net Transaction Amount
Objective:
To prove the accuracy of the calculation of the net amount.
Exercise Description:
Calculate the net amount from the GROSS_AMT, PST, and GST fields. This involves adding a Virtual field to the database and entering the appropriate field equation.
IDEA Functionality Covered:
l Field Manipulation
l Adding Virtual fields
l Displaying the format of the database
l Changing field names in a database
To calculate the net transaction amount:
1. Ensure Acc Rec March 31 2015 is the active database and Data property is selected in the Properties window.
2. On the Data tab, in the Fields group, click the Field Manipulation dialog box launcher (the arrow in the bottom right corner of the Fields group).
Alternatively, you can double-click within the Database window to display the Field Manipulation dialog box.
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3. Click Add and enter the following details:
l Field Name: NET
l Field Type: Virtual Numeric
l Field Length: Leave blank
l Decimals: 2
l Description: Net Amount
4. Click in the Parameter field to launch the Equation Editor. In the Equation Editor, enter the following equation:
GROSS_AMT - (GST+PST)
5. From the toolbar, click the Validate and Exit button to verify the syntax and exit the Equation Editor.
6. Click OK.
7. Click Yes to continue and to add the field to the database.
8. Scroll right and view the resultant field that is added as the right-most field of the database. Note that the values in this field are displayed in the color teal. This distinguishes a Virtual field from an original imported field.
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9. Place the cursor over one of the Virtual field values and note the equation in the tooltip that is displayed.
10. View the History of the database and note the recording of the Virtual field equation.
Virtual fields are not editable in any child database created from the parent database.
11. From the Properties window, click Data.
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Exercise 2K: Analyzing the Balances and Taxes by Account
Objective:
To total the Accounts Receivable and taxes by customer to prove customer balances. Identify the top ten debtors.
Exercise Description:
Use the Summarization task to total the Accounts Receivable and taxes by customer account number.
l Summarize (i.e., total) data by a key
l See the differences between the Summarization options
l Create a list of unique items in a database
IDEA Functionality Covered:
l Using Quick Summarization
l Indexing databases with a single key (Quick Index)
In this exercise, the Quick Summarization option can be used as there is only one field in the key (i.e., ACCOUNT_NO).
1. Ensure Acc Rec March 31 2015 is the active database and the Data property is selected in the Properties window.
2. On the Analysis tab, in the Categorize group, click Summarization.
The Summarization dialog box appears.
3. Select the following:
l Fields to summarize: ACCOUNT_NO
l Numeric fields to total: GROSS_AMT, GST, PST, NET
4. Click Fields.
The Fields dialog box appears. Note that no fields are selected. This stops unnecessary information being included in the summarized database.
5. Click OK to return to the Summarization dialog box.
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6. Select the Use Quick Summarization option.
7. Accept the Create database option.
This creates a database of the resultant account records. This database will be joined to the Customer Master database in a later exercise.
8. Do not select the Create result option.
This would write the results to a new Results output with drill-down and graphic capabilities. Criteria can be specified for a summarization. If asked to include transactions before a specific date, the Criteria option would be used. For this exercise, the Criteria option will not be used.
9. In the File name field, enter Balances by Customer.
10. Click OK.
11. In the output database, adjust column widths and note the following fields:
l ACCOUNT_NO: Customer accounts with balances
l NO_OF_RECS: Number of records (i.e., invoices, credits) per account
l GROSS_AMT_SUM: Gross balance by account
l GST_SUM: Total GST (Goods and Services Tax) by account
l PST_SUM: Total PST (Provincial Sales Tax) by account
l NET_SUM: Total net balance by account
In addition to providing totals for the selected Numeric fields, the Balances by Customer database shows 51 customers with balances.
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12. To identify the top ten debtors, sequence the records in descending GROSS_AMT_ SUM value by creating an index. If there is a single field in the key (i.e., GROSS_ AMT_SUM), the "quick" index option can be used as follows:
a. Double-click the GROSS_AMT_SUM field name. This will index the database on the GROSS_AMT_SUM field in ascending order.
b. Double-click the field name a second time to index in descending order.
Note that the Indices area of the Properties window displays the index GROSS_AMT_SUM/D.
The largest balance is $45,736.81 for account W025 (with 11 transactions).
13. Close all databases.
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Exercise 2L: Checking Debtors Against Authorized Credit Risk
Bright IDEAs Inc. services must be paid for in advance unless a credit check has been performed for the customer and an entry made on the authorized customer credit list.
Objective:
To test that all transactions, such as invoices, credit notes, and payments are recorded for valid customers. To identify where credit limits have been exceeded for any customer.
Exercise Description:
Import the Customer Master file that has been supplied as an electronic report file using the Report Reader module.
The Balances by Customer database will be matched (joined) to the Customer Master database. Accounts where the credit limit has been exceeded will be identified.
IDEA Functionality Covered:
l How to import a print report file using the Report Reader module within IDEA
l The need to plan for the field types, if the database is to be joined, matched, or compared with another database
l Join or match data in different databases
l The different join options
l Identify the number of mismatches on key fields
l Perform an extraction
l Find all records containing specific data
The Customer Master file has been supplied as a Print Report in text format. Report Reader is used to extract data from Print Report files, importing it into IDEA.
Print report file: CUSTOMER
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Importing the Print Report file
1. On the Home tab, in the Source Data group, click Import.
The Import Assistant appears.
To access the Import Assistant in pre-IDEA 11.2 versions, on the Home tab, in the Import group, click Desktop.
2. Select Print Report and Adobe PDF.
3. Click the Browse button. By default, the contents of the Source Files.ILB folder is displayed. It displays all the file types that can be importing using the Print Report and Adobe PDF format, such as PDF, PRN, and TXT.
4. Select Customer.txt and then click Open.
5. Click Next to open the Report Reader.
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6. Maximize the Report Reader window. Scroll through the report and notice the information that is included in the report:
l Account Number
l Name
l Address
l Credit Limit
Report Reader converts plain text, Print Report (PRN, TXT, ASC, DAT) and Adobe Acrobat PDF files into IDEA databases. Report Reader features the ability to import records that contains text that spans multiple lines.
Report Reader uses a set of layers to define the data to be imported into IDEA. The first step is to identify the Base Layer. The Base Layer is the line(s) that contains the lowest level of detail information in the report (i.e., the lines that contain most of the data that you want to specify for each record in the output IDEA database).
Once the Base Layer is defined, Append Layers that provide related information to the Base Layer can also be defined. The Base Layer and Append Layers are identified by Traps.
Traps can be Text, Numeric, Space, Non-Blank, Floating, or actual words that uniquely identify each occurrence of the specified data in the report. A trap looks for characters in the report that match the trap characters. Keep the traps as simple as possible. Common examples include a decimal point in a particular position, or a specific Date format. Do not overlook the possibility that spaces, or spaces combined with numbers or text could be the best traps.
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Defining a Base Layer
1. Scroll through the report and identify the line(s) that contain the lowest level of detailed information and highlight the lines with the cursor (click and drag). Since the address information is a multiple line field, the Base Layer will contain 5 lines.
2. Select Create a standard layer and then click Yes.
The Report Reader will display a Field Editor (the section between the two yellow lines) where the fields that need to be included in the database are defined. Scroll through the report and notice each section of data begins with an account number. You can use traps to capture the data.
Button Name Description
Text Trap
This is a pre-defined, generic trap that selects lines with alphabetical characters.
Numeric Trap
This is a pre-defined, generic trap that selects lines with numeric characters.
Space Trap
This is a pre-defined trap that selects lines that contain any blank spaces.
Non- blank Trap
This is a pre-defined, generic trap that selects lines that do not have a space, but have any numeric, text or other character.
The best trap for the Base Layer is a combination of text and numbers, based on the account number (TNNN).
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3. Place the cursor in the Anchor Editor area above the account number in the Field Editor.
4. From the toolbar, click the Text Trap button once and click the Numeric
Trap button three times.
Notice each section of data that begins with an account number is highlighted.
5. Scroll through the report to make sure all the information is highlighted.
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Identifying Field Anchors
Selecting the information that will become the data fields in the database is done by identifying Field Anchors. Using the cursor, you will highlight each Field Anchor contained in the Base Layer. Make sure the widths are large enough to include information that may be slightly off position or extremely long. Watch for items that appear as spaces in the Base Layer line, but contain values in other lines.
1. In the Field Editor (the section between the two yellow lines), click and drag your cursor across the account number to highlight the information.
The color of the selected field is highlighted in orange and all the account number fields of the detail lines in the report are highlighted.
If a mistake is made while highlighting the field and you want to remove the Field Anchor, do one of the following:
l On the toolbar, click the Remove Field Anchor button .
l Right-click the Field Anchor and then click Remove Field Anchor.
Both actions remove the Field Anchor that is active and highlighted.
Now that the Field Anchor in the Base Layer has been highlighted, it is necessary to name and detail each field. This is done using the Field Details window.
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2. In the Field Details window, enter the following details:
l Layer Name: Layer-1
l Name: ACCOUNT_NO
l Type: Character
l Offset: 5
l Actual Width: 4
3. In the Field Editor, highlight the customer name information, leaving enough room for the larger data.
4. In the Field Details window, enter the following details:
l Name: CUST_NAME
l Type: Character
l Offset: 23
l Actual Width: 28
5. In the Field Editor, highlight the address information, leaving enough room for the larger data.
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6. In the Field Details window, enter the following details:
l Name: ADDRESS
l Type: Character
l Offset: 57
l Actual Width: 33
l Multi-line: Yes
l Space to One: Yes
7. In the Field Editor, highlight the credit limit information, leaving enough room for the larger data.
8. In the Field Details window, enter the following details:
l Name: CREDIT_LIM
l Type: Numeric
l Offset: 95
l Actual Width: 7
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9. When you are done the fields should match the following table:
NAME TYPE OFFSET ACTUAL WIDTH
ACCOUNT_NO Character 5 4
CUST_NAME Character 23 28
ADDRESS Character 57 33
CREDIT_LIM Numeric 95 7
Since all the required information can be defined in the Base Layer, Append Layers will not be defined for this exercise.
10. On the toolbar, click the Save Layer button .
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Importing into IDEA
After you have saved the Base Layer you can import the data into IDEA. However, before importing the data into IDEA, it recommended to preview the entire database and run the Scan for Errors task. Scan for Errors identifies any items that may not import properly such as fields where the length is too short and fields that are not in alignment with the preceding fields.
1. On the toolbar, click the Preview Database button . The preview should look like the following example:
2. On the toolbar, click the Scan for Errors button .
Once the Scan for Errors validation is complete, save the Report Reader template for future use with similar reports.
3. On the File menu, click Save Template As.
By default, you are prompted to save the template file in the Import Definitions.ILB sub-folder of the active project.
4. In the File name field, enter Customer Master and click Save.
5. On the toolbar, click the Import into IDEA button .
6. Click Yes to proceed with the import.
7. Select the Generate field statistics option
8. Name the database Customer Master and then click Finish.
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Setting the Control Total Field
In the Properties window, click Control Total, and set the CREDIT_LIM field as the control amount field for the database.
l The control amount of 1,595,000 is displayed.
l There are 87 records (i.e., 87 customers)
Joining the Balances
IDEA enables multiple databases to be viewed at one time. This is particularly useful when joining or matching databases as it enables you to identify the common key by which the databases will be matched.
1. Open the Balances by Customer database (leaving the Customer Master database open).
2. On the View tab, in the Tabs group, and click Horizontal.
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3. Identify the field that is the common field by which the two databases are to be joined. In this case, the common field is ACCOUNT_NO.
An alternative way to create or change tab groups is by right-clicking on the database tab and selecting either New Horizontal Tab Group or New Vertical Tab Group. Notice you can also arrange the databases in a vertical format.
4. Double-click in the Database window to access the Field Manipulation dialog box.
5. Confirm that the common field is the same format (i.e., Character) in both databases. (Having checked one database, double-click the Database window in the other tab.)
The tab name with the white text is the active database.
6. Close the Customer Master database and maximize the remaining window.
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7. Ensure that Balances by Customer is selected as the active database (this is known as the primary database for the join).
8. On the Analysis tab, in the Relate group, click Join.
9. Click the Select button to choose the Customer Master database as the Secondary database.
Notice the Primary and Secondary database details in the Join Databases dialog box.
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10. Specify the common match key by clicking the Match button to display the Match Key Fields dialog box:
a. Click the Primary text box and select ACCOUNT_NO from the list of fields.
b. Note the Order text box and accept the default, Ascending.
c. Click the Secondary text box and select ACCOUNT_NO from the list of fields and then click OK.
There are five join options at the bottom of the Join Databases dialog box. These determine if only matched items are required, non-matches and so on.
MATCH OPTIONS DESCRIPTIONS
Matches only Extracts records with matches in both databases.
Records with no secondary match
Extracts records from the primary database with no matches in the secondary database.
Records with no primary match
Extracts records from secondary database with no matches in the primary database.
All records in primary file
Extracts records from primary database with or without matches in secondary database.
All records in both files
Extracts all records from both databases.
11. Select the All records in both files option.
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12. In the File name box, enter Customer Balances and Limits. The Join Databases dialog box should appear as in the image below.
13. Click OK to join the selected databases. View the output database and notice the "gaps" in the data under ACCOUNT_NO where there have not been matches on the keys.
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14. Click the History link in the Properties window and then locate and expand the section for the Join Databases task.
There are 101 records in the Customer Balances and Limits database (87 primary records + 14 unmatched primary records as discussed below).
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There are 14 unmatched primary records. These are balances for customers that are not on the Customer Master database. To identify these unmatched primary records, return to the Data property and run the Direct Extraction task on the Customer Balances and Limits database using@IsBlank (ACCOUNT_NO1), where ACCOUNT_NO1 is the account number field from the Customer Master database.
There are 50 unmatched secondary records. These are customers for whom there have been no transactions. These may be either new or dormant customers.
To identify the unmatched secondary records, run the Direct Extraction task on the Customer Balances and Limits database using@IsBlank(ACCOUNT_ NO), where ACCOUNT_NO is the account number field from the Balance by Customer database.
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Alternative Method to Identify the Unmatched Items
1. Ensure the Customer Balances and Limits database is active.
2. Locate one instance of an unmatched item (i.e., record number 26) and select the ACCOUNT_NO1 field that is "empty" (Character fields are filled with spaces).
3. Right-click over the field and click Filter by "".
All records satisfying the criterion will be displayed. To return to the whole database, right-click the Criteria link in the Properties window and click Clear. Repeat the process for ACCOUNT_NO = "", using the first record.
Credit Limits Exceeded - Extraction
Extract all customers where the credit limit has been exceeded.
1. Ensure Customer Balances and Limits is the active database and the Data property is selected.
2. On the Analysis tab, in the Extract group, click Direct.
3. In the File Name field, enter Credit Limits Exceeded.
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4. Click the Equation Editor button and enter the following equation
GROSS_AMT_SUM > CREDIT_LIM
5. From the toolbar, click the Validate and Exit button to check the syntax and exit the Equation Editor.
6. In the Records to extract area, accept the default selection of the All option to extract the records from the whole database, and then click OK.
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7. View the resultant database, record the results, and view the Field Statistics.
l There are 16 records totaling $161,345.68
l Fourteen are unmatched primary records where there is no matching account number in the Customer Master database.
l Two customers, R025 andW025, have exceeded their credit limits. To view these records, click the # of Positive Records blue drill-down value for the CREDIT_LIM field.
8. Close all databases.
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Conclusion
Matching to the authorized customer list found 14 debtors not permitted to have credit and 2 exceeded their limits.
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2.6 Audit Findings The following audit findings were reported to the client:
l The profile of accounts showed most debt to be current with only $5,465 more than 3 months old. Ten items represent 38% of the total debt.
l There are a significant number (see list) of credit items and several unallocated payments received which need to be resolved.
l Credit has been given to 14 customers without authorization and two customers have exceeded their credit limit.
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Section 3
Accounts Payable Audit and Fraud Investigation
3.1 Introduction What are the risks associated with Accounts Payable that could be addressed by computer- assisted audit procedures? And what tests can help address those risks? This section summarizes the business and audit risks that arise in the context of Accounts Payable and the potential tests that can be used to address those risks.
3.1.1 Potential Risks
The following table identifies the key risks, explains the business and audit implication of each risk and the audit objectives that could be addressed by audit tests.
RISK IMPLICATIONS AUDIT OBJECTIVES
1 Payments are made to unauthorized suppliers.
Unauthorized suppliers could represent former suppliers that supplied goods or services of unacceptable quality and should have been removed from the list of suppliers; or they could be fictitious suppliers set up by dishonest personnel to receive automated payments. Payments made to unauthorized suppliers could therefore represent either error or fraud.
Existence, Validity
2 Payments are made to individuals or employees.
Payments made to individuals or employees could represent a diversion of company payments, indicating fraud.
Existence, Validity
3 Unauthorized premiums are given to suppliers.
Unauthorized premiums may represent overpayments to suppliers in return for kickbacks.
Existence, Validity
4 Invoices are paid late.
Delays in processing Accounts Payable approvals can result in a loss of available discounts for timely remittances and understatement of liabilities for a specific period.
Cut-off,
Completeness
5 Invoices are paid on irregular dates.
Irregular payments may reflect processing errors or fraud.
Existence, Validity
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RISK IMPLICATIONS AUDIT OBJECTIVES
6 Invoices are processed twice.
Duplicate payments can result from the failure to cancel documents to prevent re-use or processing errors in Accounts Payable such as restoring a backup file twice.
Existence, Validity
7 Payments are made in a way to be undetected by audits.
Perpetrators of fraud may arrange payments to avoid detection. For example, large amounts may be split into several smaller payments to coincide with a perpetrator’s transaction approval limits or avoid limit checks on large payments.
Existence, Validity
8 Items (e.g., purchase orders, checks) are missing.
Since Accounts Payable are often not authorized for payment until there is a three-way match of purchase order, receiving document and supplier invoice, missing documents could result in Accounts Payable being understated.
Completeness
3.1.2 Potential Tests
The following audit tests are suggested when auditing an Accounts Payable system. However, the exact tests carried out for a client will depend upon the system used and the data available. Common tests include:
Mechanical Accuracy and Valuation
l Total the file. It often pays to separate debits and credits.
l Revalue foreign payables, if applicable.
l Check transaction totals to the balance on each account.
Analysis
l Stratify the size of payments and extract any exceptionally high payments.
l Analyze payment days and identify suppliers with favorable payment terms.
l If the computer system captures the approving authority for a transaction, examine the value distribution for each manager.
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Exception Tests - Existence and Validity
l Identify payments to unauthorized suppliers by matching the payments and authorized suppliers list.
l Search payments file for payees without suffixes such as "Inc", "Ltd", or "Co" in their name to identify payments to individuals.
l Test for large discounts.
l Test for duplicated invoices using value and supplier code as the key fields for one test and purchase order number for another. The second processing of invoices can be used to establish a value on the Profit/Loss (P/L) to make a fraudulent payment. (This will also pick up accidental duplication.)
l Identify payments made on Sundays or other days/dates that are not valid.
l Examine to see if amounts are being approved at or just below break points in authority level by a value distribution across the whole ledger. If approval authority is not directly available, perform subsidiary analysis by types of supplier or approving department (i.e., marketing).
l Look for split invoices to enable approval to be kept by an individual. Extract all invoices within 90% of an approved limit (preferably for a suspected manager or department) and search for all invoices from that supplier. Sort by approving manager, department and date to identify possible split invoices or summarize payments by invoice number to determine how many partial payments have been made for each invoice.
l Tests for total payments in year exceeding previous years by more than 25%.
l Test for large one-off payments to suppliers.
l Using the first five or six characters of the name, match supplier names against a list of employee surnames from a payroll or personnel file.
l Test for similar supplier names.
l Test for incomplete or unusual supplier details.
Gaps and Duplicates
l Test for missing items or gaps in the check number sequence.
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3.2 Case Scenario: Accounts Payable The Chief Financial Officer of Bright IDEAs Inc. is concerned that a member of the Accounts Payable Department is living beyond his means and that the pattern of payments in this department does not correlate to that of previous years. He has asked Dynamic Accountants to perform a special review of the Accounts Payable system to test for any irregularities.
Purchase/payments fraud is probably the most common type of fraud in an organization. It may be the simple submission of a false invoice, the reuse of another valid invoice, withholding of a credit note, or a more complex arrangement.
Many frauds involve the manipulation of the payments information on accounts within the Accounts Payable ledger. Examples include the creation in the ledger of a fictitious supplier, or branch of a genuine supplier, or reactivating a dormant account. Particularly vulnerable are miscellaneous accounts, but the fraud perpetrated on a genuine supplier’s account (with or without the supplier’s collusion) must not be overlooked. The cost must be charged somewhere and there are often general accounts that are more loosely controlled than others and accounts with high levels of transactions (i.e., purchase of material) where a fictitious item can be buried.
Many purchasing systems now are complex with automatic re-ordering so that once a supplier has been set up and/or a requisition input, payment will be processed automatically.
Staff may also be in collusion with suppliers to commit fraud by receiving gifts or kickbacks for additional business, by paying invoices on more favorable terms than other suppliers, or as per the organization’s policy on payments. Also, ordering and payment authorization levels may be exceeded by splitting invoices to payments below authorization thresholds.
IDEA can be used on several files: supplier master, purchase ledger, payments history or purchase invoices. It depends on the system, the available data, and the nature of possible frauds as to which test is best.
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3.3 Obtaining the Data The following data files will be used for this exercise:
l Accounts Payable History for the year Jan 1, 2015 – Dec 31, 2015
l Supplier Master File
When conducting fraud investigations it is important to obtain the required data without alerting the person(s) or departments under suspicion. Therefore, although our general advice is to always request the data from the client and to avoid accessing the client’s computer system to obtain data, it may be necessary to take a copy of the data files (or system) or to extract data yourself or with the assistance of the software suppliers. Alternatively, if the data you wish to investigate is available on a standard printout, arrange for the printout to be "printed" or spooled to file and use the Report Reader module in IDEA to read the required data into IDEA for testing.
In this instance, the Accounts Payable system was an Oracle database system running under UNIX. Assistance was available to determine what data was available and to write an SQL query to extract the payments history in ASCII Delimited format, a simple format to import into IDEA.
The Authorized Suppliers information is available on a Microsoft Excel worksheet. Therefore, this is also requested.
3.3.1 Requesting Data Files for Audit Purposes
The following data has been supplied from the Accounts Payable system in ASCII Delimited format as ACCPAY2015.txt:
FIELD FORMAT
Supplier Number C 8
Payee C 20
Invoice Number C 12
Invoice Date D YYYYMMDD
Amount N 8,2
Check Number N 8,2
Payment Date D YYYYMMDD
Payment Authorization Initials C 5
In the record definitions, C refers to Character, N to Numeric and D to Date. C 4 means a character field of length 4. N 8,2 is an 8-digit number with 2 decimal places.
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The following Authorized Suppliers data is supplied in the Supplier.xls file:
FIELD FORMAT
Supplier Number C 4
Supplier Name C 30
Supplier Address 1 C 25
Supplier Address 2 C 25
Supplier Address 3 C 25
Zip Code C 8
Previous Year’s Total Payments N 8,2
Data is required for the following period: Jan 1, 2015 – Dec 31, 2015.
Please supply a record layout for the ASCII Delimited file.
Please also supply the following control totals for reconciliation purposes:
l Total payments for the year 2015.
l Total discount for the year 2015.
l Number of payment transactions for the year 2015.
l Number of authorized suppliers.
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3.4 Audit Program From the available/possible tests, the audit manager decides on the appropriate tests and prepares the following Audit Program for you to complete.
3.4.1 Audit Objective
To identify payments which are suspicious or may be invalid.
AUDITING PROCEDURES ASSERTIONS WORK DONE BY (INITIALS)
EXTENT OF
TESTING
1 Obtain the data files from the client and load onto the PC.
Load IDEA and create a project for Accounts Payable for Bright IDEAs Inc.
2 Import the data and agree the total of Accounts Payable:
a) Import the Accounts Payable transactions,
b) Import the Authorized Suppliers details,
c) Compute field statistics to verify that the data has been imported correctly,
d) Select a control total field for each database, and
e) Use Discover to identify trends within the data.
Completeness
Completeness
3 Analyze the profile of the number and value of payments by numeric band to identify any unusual trends and to determine high value amounts for extractions.
4 Identify high and unusual amounts.
5 Identify further unusual payments from previous analyses including:
a) "CASH" in Payee name,
b) Round sum amounts,
c) Payments authorized by HMV, and
d) Payments made on a Sunday.
6 Test for duplicate payments and vendors/payees.
7 Test for completeness by testing for gaps in the check number sequence.
8 Identify working days on which no payments were processed.
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AUDITING PROCEDURES ASSERTIONS WORK DONE BY (INITIALS)
EXTENT OF
TESTING
9 Analyze payment terms and ensure that the payment policy for Bright IDEAs Inc. has been followed.
10 Test the validity of payments to authorized suppliers.
11 Analyze payments by supplier to identify significant changes from the previous year’s total.
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Exercise 3A: Audit Setup
Objective:
To be able to load data, run IDEA and create a project for the investigation audit within IDEA.
Exercise Description:
This exercise covers copying data into a folder, loading IDEA, creating a project, and entering client properties that will be printed on all reports.
IDEA Functionality Covered:
l Creating a project
l Entering project properties
Required Data Files:
The following data files are provided with this workbook:
l ACCPAY2015.txt - Accounts Payable history file
l Supplier.xls - Authorized Suppliers Microsoft Excel worksheet
Accessing IDEA
1. From the Windows Start menu (e.g., Windows 10), navigate to and expand the IDEA folder.
2. Click IDEA.
Exercise
Section 3: Accounts Payable Audit and Fraud Investigation
Creating the Accounts Payable Project
To facilitate housekeeping, it is recommended that a separate project be used for each audit/ investigation.
All information relating to the audit, including data files, equations, views/report definitions, templates, etc. may be stored as part of the project.
This exercise will explain how to create a project and enter project information that will be printed on all reports. Note that once a project is set, it remains the active project until changed.
1. On the Home tab, in the Projects group, click New.
2. Select the Managed project option and name the project Accounts Payable.
To create a new project in pre-IDEA 11.2 versions, on the Home tab, in the Projects group, click Create.
3. Click OK.
Accounts Payable becomes the active project, closing any previously active projects.
4. On the Home tab, in the Projects group, click Properties to change the project properties.
5. In the Project Properties dialog box, enter the following:
l Report name: Accounts Payable Investigation
l Report period: Jan 1, 2015 - Dec 31, 2015
The project properties are stored in a file called Client.inf in the project folder.
Loading Data
The following data files are provided with the workbook and are required for the Account Payable project.
l ACCPAY2015.txt - Accounts Payable history file
l Supplier.xls - Authorized Suppliers Microsoft Excel worksheet
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Use either of the following methods to add the required data files to the project:
l Use IDEA to add the data files to the project:
1. In IDEA, click the Library tab.
2. From the Current Project Library, right-click Source Files and click Add File....
3. Navigate to and select the required files.
l Use Windows Explorer to manually copy the data files to the project:
C:\Users\[UserID]\My IDEA Documents\IDEA Projects\Accounts Payable\Source Files.ILB
This is the default location within a project to store any source files.
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Exercise 3B: Importing the Accounts Payable and Authorized Suppliers Data Files
Objective:
To import the data files for testing.
Exercise Description:
You will use the Import Assistant to import the Accounts Payable Transactions file, ACCPAY2015.txt, into IDEA. The Import Assistant can determine the file format, in this case it is an ASCII Delimited format.
The Authorized Suppliers data, Supplier.xls, is provided as a Microsoft Excel worksheet. This will be imported directly into IDEA.
IDEA Functionality Covered:
l Import an ASCII Delimited file
l Import a Microsoft Excel worksheet
Importing the Accounts Payable Data
You have been provided with an ASCII Delimited file: ACCPAY2015.txt
Review the following record definition for ACCPAY2015.txt.
FIELD NAME TYPE START LEN DEC DESCRIPTION
SUPPNO Character 1 9 Supplier Number
PAYEE Character 10 17 Payee
INVOICE Character 27 12 Invoice Number
INV_DATE Date 39 8 Invoice Date
AMOUNT Numeric 47 15 2 Amount
CHECK Numeric 71 6 0 Check Number
PAY_DATE Date 77 8 Payment Date
AUTH Character 85 7 Payment Authorization Initials
Exercise
Section 3: Accounts Payable Audit and Fraud Investigation
1. On the Home tab, in the Source Data group, click Import to access the Import Assistant.
To access the Import Assistant in pre-IDEA 11.2 versions, on the Home tab, in the Import group, click Desktop.
Once loaded, the Import Assistant guides you through the process of importing the data.
2. Select Text and click the Browse button adjacent to the File name field.
The Select File dialog box appears. The Source Files.ILB folder for the active project is displayed by default.
3. Select the ACCPAY2015.txt file.
4. Click Open. The file path is added to the Import Assistant.
5. Click Next.
6. Once the data file has been selected, the Import Assistant will try to determine the data file type. The following screen will be displayed. The Delimited format is correctly identified. Click Next to proceed.
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7. The Import Assistant will try to determine the field separators and text encapsulators (if any) for the file.
Do not select the First visible row is field names option, and click Next to proceed.
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8. The Import Assistant - Field Details screen is used to define each field’s name and details in turn, including identifying which fields or areas should not be included.
You will use the following record layout to identify each field.
FIELD NAME
TYPE START LEN DEC DATE MASK
DESCRIPTION
SUPPNO Character 1 9 Supplier Number
PAYEE Character 10 17 Payee
INVOICE Character 27 12 Invoice Number
INV_DATE Date 39 8 YYYYMMDD Invoice Date
AMOUNT Numeric 47 15 2 Amount
CHECK Numeric 71 6 0 Check Number
PAY_DATE Date 77 8 YYYYMMDD Payment Date
AUTH Character 85 7 Payment Authorization Initials
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9. To identify each field using the record layout above:
a. Select the field in the data area. The active column will be highlighted.
b. Define each Field name and Type. If required, define Number of decimals and Date Mask. You can also optionally define the Description.
For the INV_DATE and PAY_DATE fields you must supply a Date Mask. The mask represents the actual format of the ASCII source data. You specify the source data format using the letters Y, M, and D and type any spaces or special characters to exactly mimic the source date format of YYYYMMDD.
c. For the NUM6 field, select the Do not import this field option.
10. Click Next to continue.
The Import Assistant - Create Fields screen appears. Create Fields allows you to add Virtual, Editable, or Multistate fields to the imported file. This can be done during the import or at any time while using IDEA. For this exercise, no fields will be added.
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11. Click Next.
The Import Assistant - Import Criteria screen appears. On this screen, you can create an equation to specify the data that is imported. For this exercise, do not enter an equation.
12. Click Next.
The Import Assistant - Specify IDEA File Name screen appears. In this final screen, you can specify the import options, save the record definition created using the Import Assistant for re-use in future audits, to specify the resultant IDEA database name.
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13. Specify the import options:
a. Leave the option to Import the database (rather than linking to the database) as IDEA will run faster when a file is imported rather than linked.
b. Select Generate field statistics on import.
c. Save the record definition. Click the Browse button and accept the default folder and definition name:
C:\Documents and Settings\[UserID]\My Documents\IDEA\Accounts Payable\Import Definitions.ILB\ACCPAY2015.rdf
d. Click Save to continue (the definition will be saved with an .rdf file extension).
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14. In the Database name box, enter Accounts Payable, click Finish.
The data will be imported into the project, opened, and displayed in the Database window.
Differences Between Importing and Linking to a Data File
Each IDEA import or link results in an IDEA master database (<filename>.imd) being created in the project folder.
However, in both cases, an IDEA database file (<filename>.imd) is created that stores all the information about the database. Importing results in a copy of the data being included in the IDEA database file.
If the option to Link to the database is selected, the original data is not included in the database file. The advantage of linking is that it can save disk space since the IDEA database file is smaller than it would be if the source data had been copied. However, both the source data file and the IDEA database file must be stored because IDEA must have access to the source data file to obtain the data needed to perform analytical functions.
Testing will be faster on imported data files.
Housekeeping is simplified when data is imported as there is only one file to backup/restore/copy/move, and it is stored in the project folder.
It is recommended that the Import option be used provided you have sufficient disk space.
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Importing the Authorized Suppliers Microsoft Excel Worksheet
The Authorized supplier’s information is provided as a Microsoft Excel worksheet: Supplier.xls. IDEA will directly import a Microsoft Excel worksheet.
IDEA imports multiple worksheets at one time, producing a separate IDEA database for each.
To import the Microsoft Excel file:
1. On the Home tab, in the Source Data group, click Import.
To access the Import Assistant in pre-IDEA 11.2 versions, on the Home tab, in the Import group, click Desktop.
2. Select Microsoft Excel and click the Browse button to navigate to and select the file.
3. In the Select File dialog box, select Supplier.xls and click Open.
4. Click Next.
5. The Import Assistant will display a preview of the data and a list of any worksheets defined within the file. Select the Address worksheet in the Select sheets to import box.
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6. Select the First row is field names option. In the Output file name box, delete the default name and enter Authorized Supplier.
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7. Click OK.
IDEA will name the new database with the prefix that has been supplied during the import followed by the name of the worksheet. For this example, the new database will be called Authorized Supplier- Address.
The Authorized Supplier - Address database will be imported, opened, and selected as the active database.
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Exercise 3C: Verifying that the Data Files Have Been Correctly Imported
Objective:
Ensure that the data is complete and agrees to the supplied control totals.
Exercise Description:
View the Field Statistics for the Numeric fields in the Accounts Payable and Authorized Suppliers-Address databases.
The statistics will be used for:
l Agreeing totals
l Getting a general understanding of the ranges of values in the database
l Testing the period of the file for completeness and cut-off
l Highlighting potential errors/areas of weakness to focus subsequent investigations
IDEA Functionality Covered:
l Field Statistics for both Numeric and Date fields
l Control Totals
Generating Field Statistics
1. Select the Accounts Payable database as the active database either by double- clicking on the file name in the File Explorer or selecting the open file list from the drop-down icon on the data sheet.
Exercise
Section 3: Accounts Payable Audit and Fraud Investigation
2. From the Properties window, click the Field Statistics. Field statistics will be displayed. Note that the following totals are automatically provided for reconciliation purposes. Ensure that your database totals agree.
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3. From the Field Type list, click Character and study the information. Note the # of Blanks for SUPPNO and PAYEE.
You can view the following field statistics for all Character fields in the database:
Field Statistic
Description
# of Blanks
Identifies the total number of empty records in the Character field. This is useful in testing for availability or missing items that can be an indicator of fraud or incompleteness. Records with blank supplier numbers, invoice numbers, purchase order numbers, or missing authorizer should be examined for completeness. Not all Character fields are expected to have data in each record. For example, it may be reasonable for a memo or description field to contain no information.
# of Categories
Essentially summarizes the values in the Character field and displays the number of unique keys within it. For example, the # of Categories for SUPPNO indicates there are 49 unique supplier numbers. The fact that the number of unique supplier numbers differs from the payee name would indicate that there may be instances of duplicate supplier numbers with different payee names and/or vice versa.
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4. From the Field Type list, click Date and study the information. Note the Earliest Date and Latest Date statistics.
Field: AMOUNT
STATISTIC VALUE COMMENT
Net Value 34,145,300.89 Total payments
Average Value 34,179,48
Minimum Value 0.00
Maximum Value 193,487.22
# of Records 999
# of Zero Items 2 These are unexpected and should be extracted and investigated.
# of Positive Records 997
# of Negative Records 0
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Field: CHECK
STATISTIC VALUE COMMENT
# of Zero Items 0 No missing check numbers
Minimum Value 701,001 First check number in sequence
Maximum Value 702,001 Last check number in sequence
Field: SUPPNO
STATISTIC VALUE COMMENT
# of Blanks 1 One missing supplier number
# of Categories 49 The number of unique supplier numbers
Field: PAYEE
STATISTIC VALUE COMMENT
# of Blanks 1 One missing supplier name
# of Categories 84 The number of unique payee names
Field: INVOICE
STATISTIC VALUE COMMENT
# of Blanks 0 No missing invoice entries
# of Categories 999 The number of unique invoice numbers
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Field: AUTH
STATISTIC VALUE COMMENT
# of Blanks 0 No missing authorization entries
# of Categories 13 The number of unique authorizers
Field: INV_DATE
STATISTIC VALUE COMMENT
# of Zero Items 0 No missing invoice dates
Earliest Date 2015/01/04
Latest Date 2015/12/06
Field: PAY_DATE
STATISTIC VALUE COMMENT
# of Zero Items 0 No missing payment dates or unpaid invoices in the database
Earliest Date 2015/01/12
Latest Date 2015/12/29
5. Export the field statistics to PDF for all fields to keep for proof of reconciliation. From the toolbar, click the Export button . Name the file AP Field Statistics.
6. From the Properties window, click Data to return to viewing the data.
7. In the Database window, click the Authorized Suppliers-Address tab name to make it the active database.
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8. View the Field Statistics for the TOT_PREV_YR (previous year’s total) field in the Authorized Suppliers-Address database. If prompted to create field statistics, click Yes. Note that the following totals have been provided with the data file for reconciliation purposes. Ensure that your totals agree.
Field: TOT_PREV_YR
STATISTIC VALUE
Net Value 30,202,660.57
Average Value 629,222.10
Minimum Value 0.00
Maximum Value 1,397,587.06
# of Records 48
# of Zero Items 2
# of Positive Records 46
# of Negative Records 0
9. Export the field statistics to PDF for all fields to keep for proof of reconciliation. From the toolbar, click the Export button. Name the file Supplier Field Statistics.
10. From the Properties window, click Data to return to viewing the data.
Setting the Control Amount for Each Data File
1. Ensure the Accounts Payable database is the active database and the Data property is selected in the Properties window.
2. From the Properties window, click Control Total and select AMOUNT as the control field.
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3. Click OK.
According to the client, the control total amount of 34,145,300.89 should be displayed.
4. Select the Authorized Supplier-Address database as the active database.
5. Select the TOT_PREV_YR field as the control amount field.
6. Click OK.
According to the client, a total of 30,202,660.57 should be displayed.
7. Close the Authorized Supplier-Address database.
Conclusion
We have verified the accuracy of the totals for the client’s data files.
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Exercise 3D: Identifying Trends, Patterns, Duplicates, and Outliers with Discover
Objective:
Run the Discover task to analyze the current database and present any results on a dashboard for further analysis.
Exercise Description:
In this exercise, you will be able to analyze the Accounts Payable database to look for patterns, duplicates, and outliers. You will also learn how to build a dashboard to display the type of information you wish to present.
l Examine dashboard results from Discover
l Drill down through the charts and field statistics to view the detail transactions
l Modify the chart and field statistics properties to customize the results view
l Copy a chart to the clipboard to use in 3rd party applications
Dashboard Features
Discover is a data profiling task that analyzes the active database and provides insights into the data through dashboards. The dashboard consists of four charts and six field statistics. Using a proprietary technology, Analytic Intelligence, Discover displays results in the dashboard that can help in identifying trends and outliers within the database. Discover can be used as a starting point for interrogating the data to determine areas of focus.
Running the Discover Task on the Accounts Payable Database
1. Ensure the Accounts Payable database is the active database and the Data property is selected in the Properties window.
Exercise
Section 3: Accounts Payable Audit and Fraud Investigation
2. On the Analysis tab, in the Visualization group, click Discover.
An window appears indicating that the Discover task is initializing. Once initialization is complete, the Dashboard window appears containing field statistic and chart panels.
Once a field statistic is generated and displayed in a field statistic panel, you can click on that panel to drill down to the underlying data (if applicable). Not all field statistics are available for drill down, such as NetValue and Average Value that compute all records in the database.
3. Position your cursor in the # of Zero Items field statistic panel.
Note that a Properties button becomes visible in the top right corner. If the panel color changes, this indicates that you can click on the panel and drill down to underlying data.
4. Click in the # of Zero Items field statistic panel to drill down to the underlying data.
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5. Click the Restore button to exit the drill down window and return to the dashboard.
6. Click on each field statistic panel to drill down to the underlying data.
Chart panels can display treemaps, scatter, bar, column, line, and pie charts. Chart panels have a Maximize/Restore toggle button that lets you expand the selected panel to its maximum size or restore it back to its original size. In a maximized state, you can only see the selected panel in the dashboard. The other panels are hidden. The larger panel display lets you focus on the associated chart information within the panel. You can then restore the panel to its original size and view the other panels in the dashboard.
7. Position your cursor in the Accounts Payable: Count of records by AMOUNT
(STRATIFIED) chart. From the panel toolbar, click the Maximize button .
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8. Click a bar on the chart to drill down to the underlying data.
9. Click the Chart View button to exit the drill down data and return to the chart.
10. Click the Restore button to exit the maximized chart view.
Modifying Charts and Field Statistic Panels
Although the Discover task is designed to analyze the active database and automatically create a dashboard displaying insights from the data, it is possible to edit the Chart and Field Statistic panels once the Dashboard window has been created and save it as a template. This option gives you the ability to design a dashboard that displays information you want to view.
Modifying Field Statistic Panels
1. Position your cursor in the # of Zero Items field statistic panel.
Note that a Properties icon becomes visible in the top right corner.
2. Click the Properties button .
The Field Statistic Properties dialog box appears.
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3. From the Field drop-down list, select INVOICE.
Note the field statistic values for # of Duplicates and # of Unique Values are not displayed by default.
4. Click the Calculate button associated with # of Duplicates.
5. Click Save.
The field statistic panel is updated on the dashboard.
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Modifying Chart Panels
When you position your cursor in a chart panel, the panel toolbar is displayed in the top right corner.
Button Name Description
Properties Modify chart properties.
Copy to Clipboard Copy the image of a chart to the Windows Clipboard.
/ Grid View/Chart View toggle
Toggle between displaying data as a chart or a table.
/ Maximize/Restore toggle
Toggle between maximizing a chart to full size or restoring it to its original size.
1. Position your cursor in the Accounts Payable: Count of records by INV_ DATE (STRATIFIED) chart to display the panel toolbar.
2. Click the Properties button .
The Chart Properties dialog box appears.
3. From the Group by drop-down list, select AUTH.
4. From the Statistic drop-down list, select Sum.
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5. From the Field to Sum drop-down list, select AMOUNT.
6. Click Save.
Saving the Dashboard
The dashboard displays the edits you made to the field statistic and chart panels. You can now save the dashboard.
1. From the Dashboard window title bar, click the Save button .
The Save dialog box appears.
2. In the File name field, enter AP Dashboard and click Save.
The dashboard is saved as an .idash file in the Visualization.ILB sub-folder in the active project. You can access the file from the Library window in IDEA.
3. Click the Library tab to display the Library window.
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4. From the Library toolbar, click the Refresh button .
The saved dashboard now appears under the Visualization Library group.
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Exercise 3E: Analyzing the Profile of Payments
Objective:
Gain a profile of the number and value of payments by numeric bands to identify any unusual trends and to determine the high-value transactions for extractions.
Exercise Description:
In this exercise, you will stratify the payment amounts in suitable bands from the minimum to the maximum payment amounts.
l Examine the resultant stratification report
l Identify potential invoice and payment splitting below authorization levels
l Chart the data and then print or save the chart
l Drill down through any chart sector to display its composite payments
IDEA Functionality Covered:
l Plan for running a Stratification task
l Enter the parameters for the Stratification task
l Chart the results view analysis
l Drill down on result and chart sectors
l Save a chart
Stratifying the Payment Amounts
1. Ensure Accounts Payable is the active database and the Data property is selected in the Properties window.
2. View the Field Statistics for the AMOUNT field to determine the minimum and maximum values of the population.
3. From the Properties window, click Data to return to viewing the data.
Exercise
Section 3: Accounts Payable Audit and Fraud Investigation
4. On the Analysis tab, in the Categorize group, click Stratification.
The Stratification dialog box appears.
5. In the Fields to stratify, select AMOUNT.
6. In the Fields to total on box, select AMOUNT.
7. Confirm the increment is set to $10,000 (this may be changed when required).
8. Click the first row of the spreadsheet area which will fill with 0 - 10000. Click and drag down to row 10. The bands will automatically fill with the increment.
9. Change the increment to $50,000 and complete the final two bands as listed below.
>= LOWER LIMIT < UPPER LIMIT COMMENTS
0 10,000
10,000 20,000
20,000 30,000
40,000 50,000
50,000 60,000
60,000 70,000
70,000 80,000
80,000 90,000
90,000 100,000
100,000 150,000 Increment change to 50,000 bands
150,000 200,000
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10. Do not select the Create Database option.
11. Click OK.
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12. The results of the Stratification are displayed in a new Stratification Results output of the Database window. Inspect the Stratification Results output for the AMOUNT field.
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13. To export the Stratification Results output as a report, from the Results output toolbar, click the Export button . Name the file Stratification and click Save.
The exported file is saved in the Results Library group of the active project.
14. Double-click the file to open it.
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15. To see the Stratification results in a graphical format, click the View the result chart button .
You can use the graphing toolbar to adjust the graph properties including the type of graph, the colors of the graph objects, as well as the graph labels and legends. The graph can be displayed in 2-dimensions or 3-dimensions. Select 3D/2D to select the preferred view. To return to the Results output, click the View the result chart button again.
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16. Field statistic information is available for each stratum in the result. To view the Field Statistics for a stratum, left-click on the stratum to highlight it. Then select Display Field Statistics.
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17. You can also drill down to see the detail records that comprise any strata. Click any strata in the chart to highlight it, and then select Display Records.
Any of the above-mentioned Results outputs can be saved by clicking the Save button to create a new database.
As we would expect there are many low value payments, tapering off to a small number of high value payments. However, there is an exceptionally high number (and hence value) of payments in the $70,000 - $80,000 band.
As the Chief Financial Officer has informed you that the maximum payment value that can be authorized without his approval is $80,000, this is likely to indicate that invoices and/or payments are being split to circumvent this limit.
Payments in the $70,000 to $80,000 band will require further investigation, in addition to the staff authorizing the purchases and payments.
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If you want to redo your Stratification task, perhaps to change some of the stratum ranges, you do not have to start the Stratification task over again. If the Results output is active, and you have not done any other analysis on the database, you can re-run the last result simply by clicking Re-Run task on the Analysis tab. This will re-open the analysis dialog box that created the current Results output. In this case, it will re-open the Stratification dialog box previously used settings still in place.
18. Return to the Results output by clicking the Cancel button.
Conclusion
There are a significant number of payments just below the $80,000 authorization level that require investigation.
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Exercise 3F: Identifying High and Unusual Payments
Objective:
l To identify all high value items for testing.
l To identify items which do not appear to match the profile for payments.
Exercise Description:
From the Stratification analysis, we identified that there was an unusually high number of payments between $70,000 - $80,000 and that there were 3 payments greater than $100,000. These will be extracted for investigation.
IDEA Functionality Covered:
l Extract data with a specified criterion
l Use the Equation Editor
Using the Accounts Payable database, extract all high and unusual payments as follows:
1. Select the Accounts Payable database as the active database with the Data property selected in the Properties window.
2. Select the Direct Extraction task by clicking on the relevant button on the Analysis tab.
The Direct Extraction dialog box appears.
3. In the File Name field, enter Unusual and High Payments.
4. Click the Equation Editor button and enter the following equation:
AMOUNT >= 70000 .AND. AMOUNT < 80000 .OR. AMOUNT > 100000
l AMOUNT >= 70000 .AND. AMOUNT < 80000 will identify the unusually large number of payments between $70,000 - $80,000.
l AMOUNT > 100000 will identify high value payments.
5. Once the equation has been entered, check the syntax by clicking the Validate button.
6. Click the Validate and Exit button.
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7. In the Records to extract area, accept the default selection for the All option to extract the records from the whole database.
8. Click OK to run the extraction.
There should be 87 records totaling $6,850,932.26.
There are several large values and round sum payments to Supplier M100. Note the different variants of the payee name, especially the occurrence of "Cash" within the name.
Many of the payments were authorized by HMV. However, on further investigation it is determined that HMV may only authorize payments up to $20,000.
9. Close the Unusual and High Payments database.
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Exercise 3G: Identifying Exceptional Transactions
Objective:
To identify any unusual transactions.
Exercise Description:
From the previous test, further investigation will be required and the following extractions will be carried out:
l All payments with the PAYEE name containing "CASH"
l All round sum payments
l All payments authorized by HMV
l All payments made on a Sunday
In addition, a digital analysis of the amount field will be performed using the Benford’s Law task.
IDEA Functionality Covered:
l Performing multiple extractions with a single pass through the database
l Using several @Functions in equations
l Finding help on the syntax of @Functions
l Using the Benford’s Law task
Payments with "CASH" in the Payee Name
1. Using the Accounts Payable database, from the Analysis tab, in the Extract group, click Direct.
2. Change the file name to CASH in PAYEE Name.
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3. Click the Equation Editor button.
The middle panel in the Equation Editor lists the @Functions. The required equation is built using the following @Functions:
@Isini("text", field)
Used to search for text within a Character field. This @Function is NOT case sensitive.
@Strip(field) Used to remove punctuation and spaces from the text
4. The following equation will be used for the extraction:
@Isini("CASH",@Strip(PAYEE))
This activity can also be done using the IDEA Search task.
When you select the required@Function, IDEA inserts the @Function name and brackets (i.e., @Isini()) with the cursor inserted within the brackets.
5. From the toolbar, click Validate and Exit button to check the syntax and exit the Equation Editor. Do not run the extraction yet.
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Round Sum Payments
1. Click on the second extraction line in the Direct Extraction dialog box and note that a default name is supplied for a second extraction.
2. Change the file name to Round Sum Amounts.
3. Click the Equation Editor button.
4. Extract all round sum 1000s in the AMOUNT field using the following equation:
AMOUNT % 1000 = 0
Select the MOD button to insert the % symbol into the equation. This equation will return items where the remainder when dividing by 1000 is 0 (zero).
% is used in IDEA, as with many other programming languages, as the modulus symbol, not the percentage symbol.
5. From the toolbar, click Validate and Exit button to check the syntax and exit the Equation Editor. Again, do not run the extraction yet.
Payments Authorized by Manager HMV
1. Click on the third extraction line in the Direct Extraction dialog box and note that the default name is supplied for a third extraction.
2. Change the file name to Authorized by HMV.
3. Click the Equation Editor button.
4. Extract all payments authorized by HMV using the following equation:
@Upper(@Strip(AUTH)) = "HMV"
l @Upper(field) converts the contents of a Character field to uppercase.
l @Strip(field) removes spaces and punctuation from the field.
5. From the toolbar, click Validate and Exit button to check the syntax and exit the Equation Editor. Again, do not run the extraction yet.
Test for Payments Processed on a Sunday
1. Click on the fourth extraction line in the Direct Extraction dialog box and note that a default name is supplied for a fourth extraction.
2. Change the file name to Sunday Payments.
3. Click the Equation Editor button.
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4. Enter the following equation:
@Dow(PAY_DATE) = 1
Use @Dow as follows:
@Dow(date field) Used to return the day of the week where:
1 = Sunday
2 = Monday etc.
5. From the toolbar, click Validate and Exit button to check the syntax and exit the Equation Editor.
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6. Click OK to run all four extractions with a single pass through the database, potentially saving considerable time on large databases.
View the resultant databases in turn, recording and checking your results against the solution provided.
Note that the CASH in PAYEE name database is the active database. Other databases must be opened from the File Explorer.
EXTRACTION # OF RECORDS
AMOUNT COMMENT
CASH in PAYEE name
20 899,339.44 Many are authorized by HMV and VST
Also note that the Payee name is not consistent with the Supplier Number
Round Sum Amounts
7 375,000.00 Includes zero-value checks
Authorized by HMV 399 13,826,403.35 Many greater than the maximum authorization limit of $20,000 for HMV
Sunday Payments 77 3,330,645.60 29 are authorized by HMV
7. Close all databases.
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3.5 Applying Benford’s Law to Identify Exceptional Items Frank Benford was a physicist at GE Research Laboratories in the 1920’s. He determined that the first pages of the log table books were used more than the back pages. The first pages contain lots of numbers with low first digits. The first digit is the left-most digit in a number.
Benford collected data from 20 lists of numbers totaling 20,229 observations. He found that the first digit of 1 occurred 31 percent of the time. Using integral calculus, he calculated the expected digit frequencies that are now known as "Benford’s Law." It took Frank Benford six years to perform his analysis and develop his law of expected digit frequencies.
The Benford’s Law task in IDEA can provide a valuable reasonableness test for large data sets. IDEA only tests items with numbers over 10.00. Number sets with less than 4-digits tend to have more skewed distributions and do not conform as well to Benford’s Law. Positive and negative numbers are analyzed separately because abnormal behavior patterns for positive numbers are very different from those for negative numbers.
To run a Benford’s Law analysis on the Accounts Payable database:
1. On the Analysis tab, in the Explore group, click Benford’s Law.
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2. In the Benford’s Law dialog box, select the AMOUNT field as the field to analyze. Accept the other default options to Include Values that are positive, Show boundaries, Mean absolute deviation and perform all seven Analysis Types (First Digit, First Two Digits, First Three Digits, Second Digit, Last Two Digits, Second Order and Summation). Clear the Suspicious check boxes.
3. Click OK to perform the analyses.
The Benford results become active. Other databases must be opened from the File Explorer window.
4. From the File Explorer, open the Benford First Digit database.
5. On the Analysis tab, in the Categorize group, click Chart to graph the data.
The Chart dialog box appears.
6. In the Y field(s) list box, select ACTUAL.
7. In the X axis title field, enter Digit Sequence.
8. In the Y axis title field, enter Count.
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9. In the Chart title field, enter AMOUNT - First Digit - Positive.
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10. Click OK.
The Chart Data Results output becomes active.
The first digit graph shows a spike in the digit 7 results.
The First Digit test is the test of first digit proportions. The first digit of a number is the left-most digit in the number. Zero can never be a first digit. This is a high-level test. Analysts will not usually spot anything unusual unless it is blatant. This is a test of goodness-of-fit to see if the first digit actual proportions conform to Benford’s Law. The First Digit test is an overall test of reasonableness. The upper and lower bounds are merely guidelines for the auditor. The First Digit graph could show a high level of conformity, but the data set could still contain errors or biases.
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11. From the Properties window, click Data to return to the Benford’s First Digit database that was created as part of this analysis.
The DIFFERENCE field shows the difference between the expected occurrences of the digits and the actual occurrences of the digits. When the DIFFERENCE field is indexed (double-click the field name) in descending order, the digit 7 results show the largest positive difference (positive spike).
This result warrants further investigation as the Chief Financial Officer has indicated that any items over $80,000.00 require additional approval. This spike could be indicating an abnormal level of items being processed just below the additional approval level.
12. Close the Benford First Digit database.
13. To view the Benford’s Law analysis result again, open the Accounts Payable database and select Benford in the Results area of the Properties window.
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14. To view the First Two Digit graph, click on the button on the Results toolbar.
The 79, 76, and 75 two-digit combination spikes are clearly visible in this graph.
The First Two Digit test is a more focused test. The first two-digit numbers are the left-most two digits. There are 90 possible two-digit combinations ranging from 10 to 99. This test is performed to find anomalies in the data that are not apparent from either the First Digit test or the Second Digit test when viewed on their own. A spike occurs where the actual proportion exceeds the expected proportion as predicted by Benford’s Law. Positive spikes (above the Benford’s curve) represent excessive duplication. One of the objectives of this test is to look for spikes that conform to internal thresholds, such as authorization limits.
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15. Look at the transactions that comprise the 79 two-digit combination by clicking on the graph and selecting Display Records to drill down to the transactions.
Notice the number of transactions just under the $80,000 approval limit.
Of the 22 transactions that make up the 79 two-digit combination, 17 are between 79,000 and 80,000. Each of the payables clerks authorized some of the 17 transactions, but HMV was responsible for the bulk of these transactions having authorized 8 out of 17.
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Of the 16 transactions that make up the 76 two-digit combination, 10 are between 76,000 and 77,000. Of these 10 transactions, HMV authorized 7 transactions.
Of the 15 transactions in the 75 two-digit combination, 13 are between 75,000 and 76,000. Like before HMV authorized 6 of the 13 transactions. In addition, there are 5 transactions for exactly 75,000. Out of these 5 transactions, 4 were payments to companies with "Cash" in the payee name. Each of these 4 transactions was paid within a few days of their invoice date, clearly a violation of company policy.
16. Open the Benford First Two Digits database that was created as part of the analysis. Index the DIFFERENCE field in descending order.
Notice the large positive differences (positive spikes) in the first two-digit combinations of 79, 76, and 75. These were the underlying cause of the spike in digit 7 results in the First Digit graph.
Due to the size of this data set, several transactions were identified for further investigation using just the First Digit and First Two Digit analysis. In larger data sets, a finer filter of transactions is necessary.
Where there are 90 possible two-digit combinations, there are 900 possible three-digit combinations from 100 to 999. The First Three Digits test is a highly focused test that will give the analyst relatively smaller sections due to abnormal duplication and allow for a more narrowly focused analysis. This test is also valuable to look for spikes just below internal and psychological thresholds such as authorization limits. To make the most effective use of this test, the source data set should normally exceed 10,000 records.
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17. Open the Benford First Three Digits database that was created as part of the analysis. Index the DIFFERENCE field in descending order.
The "750" digit combination has the largest negative difference (positive spike).
18. Close all databases.
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3.6 Data-basedConditions for Benford’s Law
3.6.1 Geometrical Series
The mathematical pre-condition for the examination of a data supply based on Benford’s Law is that the data supply is based on a geometrical series (i.e., it is presented as Benford Set). This condition is rarely met. Experience shows; however, that data must only partially meet this condition, i.e., the constant increase, percentage-wise of an element compared to the predecessor must only be met partially. Otherwise, this would mean that no number may occur twice which is quite improbable in the case of business data supplies, however, the pre-condition is that there is at least a 'geometrical tendency'.
3.6.2 Description of the Same Object
The data must describe the same phenomenon. Examples are:
l The population of cities
l The surface of lakes
l The height of mountains
l The market value of companies quoted on the New York Stock Exchange
l The daily sales volume of companies quoted on the New York Stock Exchange
l The sales figures of companies
3.6.3 Unlimited Data Space (Non-Existence of Minima and Maxima)
The data must not be limited by artificial minima and maxima. A limitation to exclusively positive numbers (excluding 0) is permissible if the figures to be analyzed do not move within a certain, limited range. This applies, for example, to price data (e.g., the price of a case of beer will generally always range between 15 and 20 dollars) or fluctuations in temperature between night and day.
3.6.4 No Systematic Data Structure
The data must not consist of numbers following a pre-defined system, such as account numbers, telephone numbers and social security numbers. Such numbers show numerical patterns that refer to the intentions of the producer of the number system rather than to the actual object size, represented by the number (e.g., a telephone number starting with a 9 does not mean that this person possesses a bigger telephone).
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Basically, data complies best with Benford’s Law if it meets the rules mentioned above, namely that the data consists of large numbers with more than 4 digits and the analysis is based on a sufficiently large data supply. A large data supply is necessary to come as close to the expected numerical frequencies as possible. For example, the expected frequency of the digit 9 in any data supply is 0.0457. If the data supply consists of only 100 numbers, the numbers which have a 9 as their first digit may be 5% of the data supply. Thus, in the case of a small data supply, there may be an over-proportional deviation from Benford’s Law. In large data supplies, the numerical distribution is increasingly closer to the expected frequencies.
If the data supply has, or just roughly has, the characteristics mentioned above it can be analyzed based on Benford’s Law. However, the results of the Benford analyses are not interpretable based on Benford’s Law. As stated before, the expected frequencies according to Benford’s Law often represent, in the practical use, nothing more than a type of benchmark for the observed frequencies. Since the observed frequencies will only be compared with the legality discovered by Benford, not interpreted accordingly, it is not necessary that all conditions mentioned above be met.
In fact, the analysis results will help the auditor interpret the personal expectation of the auditor, without including the reference value according to Benford in the argumentation. If, for example, the personal expectation of the user is that the starting digit 4 must occur twice as often in the analyzed data than the starting digit 2, the results of the analyzed values must not be compared with the expected frequencies according to Benford but with the individual expectation of the user.
The application of Digital Analysis and the Benford Module is also permissible in the framework of Data Mining when certain distinctive facts in a data supply are measured against the personal expectations of the user and interpreted according to them. In this case it is not necessary for the data that is to be analyzed, to create a Benford Set in a strict sense. In fact, it is permissible under these circumstances to analyze the numerical distribution of the leading digits of each data quantity and to interpret it independent of Benford’s Law.
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Exercise 3H: Test for Duplicate Payments and Records
Objective:
To test for duplicate payments and records.
Exercise Description:
The Chief Financial Officer, Mr. Cuthbert is concerned that there are many more payments to certain suppliers compared to previous years. He is concerned that certain suppliers may be paid more than once for invoices, or that invoices are being resubmitted. He has asked you to test for duplicate payments. In addition to the duplicate payments being made to suppliers, Mr. Cuthbert is interested to know if there are any duplicate payments made to suppliers with slightly different names.
IDEA Functionality Covered:
l Select fields to test for duplicates
l Specify a key (or sequence order)
l Test for duplicates
l Use Duplicate Key Detection task to check for supplier numbers with multiple payee names
l Use Duplicate Key Exclusion task to test for supplier numbers with multiple payee names
l Use Fuzzy Duplicate task to check for multiple supplier names that look similar
Test for Duplicate Transactions
1. Open the Accounts Payable database.
2. On the Analysis tab, in the Explore group, click Duplicate Key and then click Detection.
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3. There are two options for duplicate testing: Output duplicate records or Output records without duplicates. For this test, select Output duplicate records to get a database containing any records that are duplicated.
Consider which field or fields should be tested for duplication (a maximum of 8 fields may be selected). In this case, duplicate payments are likely to be identified by payments of the same amount to the same supplier; therefore, these fields will be specified in the key.
4. Click Key and select the following fields:
l SUPPNO— Ascending
l AMOUNT — Descending
5. Click OK in the Define Key dialog box.
6. Do not specify criteria (i.e., only payments for a specified range of dates) for the test.
7. In the File name field, enter Duplicate Payments.
8. Click OK in the Duplicate Key Detection dialog box to run the test.
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9. View the resultant database of duplicates payments.
There are 5 duplicate records shown, including:
l 3 payments of $75,000 to supplier M100
l 2 payments of $145.50 to supplier P007
Although the supplier number and amount are the same, other information in the record is different. These will all require a follow-up to determine if they are genuine duplicates.
Note the different invoice number patterns and that all payments to supplier M100 are made within a few days of the invoice date.
Other possible tests include testing for:
l Duplicate supplier invoice numbers (i.e., test for the same supplier number and invoice number)
l Duplicate purchase order numbers (unless this is validated in the system)
10. Close the Duplicate Payments database.
Test for Supplier Numbers with Multiple Payee Names
1. Open the Accounts Payable database.
2. On the Analysis tab, in the Explore group, click Duplicate Key and then click Exclusion.
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3. In the Duplicate Key Exclusion dialog box, enter the following settings:
l Fields to match: SUPPNO
l Field that must be different: PAYEE
l Do not select the Output all duplicate records option
l File name: Suppno Multiple Payees
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4. Click OK to perform the test.
There are 121 records where the same supplier number has different payee names. These records total $4,552,803.13.
This task indicates that there is a weakness in the company’s internal controls involving the maintenance of supplier information.
5. On the Analysis tab, in the Tasks group, click Re-run.
6. Select the Output all duplicate records option and change the File name to Suppno Multiple Payees - All Duplicate Records.
7. On the View tab, in the Tabs group, click Vertical. Review the two Duplicate Exclusion databases. The second database contains all the records with duplicate SUPPNO, as long as within each set of duplicates, the PAYEE is not the same for all the records.
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8. Close all databases.
Test for Payments to Suppliers with Similar Naming Conventions
Perform a Fuzzy Duplicate task to test for multiple payee names that look similar. The Fuzzy Duplicate task identifies records within a Character field that look similar but are not exact. It identifies possible false negative records that may not get detected by a Duplicate Key Detection or Duplicate Key Exclusion task.
The Fuzzy Duplicate task can be used to look for items that are similar due to:
l Data entry errors (e.g., "Compny" instead of "Company")
l Inconsistencies in recording information (e.g., "John Smith" vs. "Johnathan Smith")
l Fraud (e.g., similar address, first names, and last names)
How similar items are when the task is executed depends on the similarity degree, which can be assigned by the user.
1. Open the Accounts Payable database.
2. On the Analysis tab, in the Explore group, click Duplicate Key and then click Fuzzy.
3. There are three fuzzy output options. Select Fuzzy matches to have IDEA identify transactions that are similar.
Consider which field or fields should be tested for fuzzy duplication. You can select a maximum of three fields. In this case, duplicate payments may be identified by payments to the same payee that have been entered into the system with different spelling.
4. Click Key to access the Define Fuzzy Match Key dialog box.
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5. From the Character fields list, select PAYEE and click Add.
6. In the Define Fuzzy Match Key dialog box, click OK.
7. In the Settings section, clear all check box options.
l The Allow records in multiple fuzzy groups option assigns a record to more than one fuzzy group, based on the similarity degree that is set.
l The Include exact duplicates option looks for exact duplicates in addition to similar records in the fuzzy groups.
l The Match case option treats uppercase and lowercase characters as different characters. If the check box is cleared, then upper and lower case characters are viewed as identical.
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8. Accept the default Similarity degree(%) setting (80%).
The similarity degree slider bar is adjusted according to how similar you want the records to be. The range is from 60% to 99%. If the slider is moved all the way to the right (99%), the more similar the records will be in the fuzzy group. Conversely, if the slider is moved all the way to the left (60%), the larger the fuzzy groups will be, because they will contain records that are less similar.
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9. In the Fuzzy Duplicate dialog box, click OK.
There are 6 records identified as having similar payee names.
A fuzzy group is a cluster of records that are similar to each other, based on the degree of similarity defined in the Fuzzy Duplicate task. A record can be similar to many records and; therefore, be included in more than one fuzzy group. This is illustrated after the Fuzzy Duplicate task is performed, where two fields that are created called GROUP_ID and GROUP_NAME. The GROUP_ID is the number that represents each group of similar records. Each group is given a name, which is the payee name in each record that represents the core group.
The SIMILARITY_DEGREE is the value representing the degree of similarity between each payee and it’s related group. In this task, the values can range from 1 (exact duplicate) to 80% similarity.
For this test, the payee "Matt Cash & Co" appears twice, yet is spelled differently. The invoice number for each instance appears to be the same, but is not 100% identical.
10. Close the Fuzzy Duplicate database.
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Exercise 3I: Searching for Gaps in the Check Number Sequence
Objective:
Test for completeness.
Exercise Description:
An auditor looking at the Bank Reconciliation is concerned that not all the checks have been recorded on the system. Test for missing check numbers in the sequence.
The Gap Detection task is used to test for missing items in a numeric list.
IDEA Functionality Covered:
l Rules for testing for missing items
l Gap Detection task
l Where to view the results
l How to export or print results
To test for missing items in a numeric list:
1. Ensure the Accounts Payable database is the active database and the Data property is selected in the Properties window.
The field CHECK contains the check number.
2. It is possible to test for gaps in Numeric or Date fields or in a numeric sequence within a Character field. To view the format of the database, double-click over the Database window to display the Field Manipulation dialog box.
3. View the format of the CHECK field and note that its field type is Numeric. Click Cancel to close the Field Manipulation dialog box.
4. On the Analysis tab, in the Explore group, click Gap Detection.
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5. From the Field to use drop-down list, select CHECK to test for gaps.
You can specify a Criteria for the test (i.e., only checks issued for a date range); however, for this exercise, leave it empty.
6. Test the whole range of check numbers by accepting the default selection of the All option.
The value in the Starting key value and Ending key value boxes are provided from the minimum and maximum values held in the Field Statistics.
7. Accept the Gap Increment of 1. If required, this option can be modified.
8. Click OK.
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9. The Gap Detection Results output appears in the Database window. To view missing checks, click the + sign located on the left side of the check sequence.
There are 2 gaps in the sequence. However, there are 5missing checks as can be seen from the inclusive ranges, i.e., 701805, 701997, 701998, 701999, 702000.
The results are saved in the IDEA database file. You can export the Results output to PDF or Microsoft Excel.
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10. From the toolbar, click the Export to Excel button . Name the file Accounts Payable, accept the default file type and location, and click Save.
11. From the toolbar, click the Print drop-down arrow and then click the Print Preview button to view the report.
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12. Close the Print Preview window by clicking the "X" in the top right corner.
13. From the Properties window, click Data to return to viewing the database.
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Exercise 3J: Searching for Gaps in the Check Date Sequence
Objective:
Test for days on which there have been no payments.
Exercise Description:
The auditors have been informed that a check payment run is processed each day. Therefore, a test should be performed to identify days on which no payments are made.
The Gap Detection task is used to test for missing items in a date range.
IDEA Functionality Covered:
l Gap Detection task
l Entering holiday dates for exclusion
l Where to view the results
l How to export or print results
To test for missing items in a date range:
1. Ensure the Accounts Payable database is the active database and the Data property is selected in the Properties window.
The field PAY_DATE contains the check payment date.
2. On the Analysis tab, in the Explore group, click Gap Detection.
3. Select the PAY_DATE field to test for gaps.
4. Select the Ignore weekends option.
5. Select the Ignore holidays option.
6. Set the holiday dates to be ignored by clicking the Set Holidays button.
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7. To add a new date, from the toolbar, click the New button and then click the
Browse button to access the calendar control. Navigate to and select the required holiday dates to add to the list.
The following dates are suggested:
l January 1, 2015
l December 25, 2015
However, please enter additional dates or modify these dates if preferred.
8. Click OK to return to the Gap Detection dialog box.
9. In the Result name box, enter Pay Date Gaps.
You can specify a Criteria for the test (i.e., only checks issued for a date range); however, for this exercise, leave it empty.
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10. Test the whole range of check payment dates by accepting the default All.
The values in the Starting date value and Ending date value boxes are provided from the Earliest date and Latest date held in the field statistics.
11. Click OK.
The Pay Date Gaps Results output of the Database window becomes active and displays the results.
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12. Save the result as a PDF file by clicking the Export button and name the file Date Gap.
The Date Gap.PDF file can be viewed, printed, or emailed.
There are 62 gaps in the sequence. However, there are 85 days on which no payments have been made.
The results are saved with the database.
The results may be printed either by clicking the Print button on the toolbar or through the Print Preview option.
13. Click the Print Preview button on the toolbar and view the report.
14. Close the Print Preview window. From the Properties window, click Data to and return to the database.
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Exercise 3K: Analyzing Payment Days to Identify Favorable Terms to Suppliers
Objective:
To analyze payment terms and ensure that Bright IDEAs Inc.’s policy on payment is being strictly adhered to.
Exercise Description:
It is suspected that certain suppliers are rewarding staff for prompt payment of invoices. The number of days between payment and invoice will be calculated and then analyzed.
IDEA Functionality Covered:
l Adding a Virtual field:
l Using the @Age function to calculate the number of days between dates
l Using the @Between function to test for a number between a range
l Indexing a database
Calculating the Number of Days for Payment
1. Ensure the Accounts Payable database is the active database and the Data property is selected in the Properties window.
2. Double-click in the Database window to load the Field Manipulation dialog box.
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3. Click Add to add the following Virtual field:
l Field Name: PAY_DAYS
l Field Type: Virtual Numeric
l Field Length: Do not enter
l Decimals: 0
l Description: Number of days to pay invoice
4. Click in the Parameter cell to load the Equation Editor and enter the following equation: @Age(PAY_DATE , INV_DATE).
@Age(Date1,Date2) calculates the number of days between the specified dates (fields or date constants). Enter the later date first to report a positive number of days as a difference.
5. From the toolbar, click the Validate and Exit button , returning to the Field Manipulation dialog box.
6. Click OK to add the Virtual field.
7. Click Yes to continue.
8. View the results in the new PAY_DAYS field. It will be the right-most column in the database.
The color of this field (the default color is teal) indicates that the field is a calculated field and not an original imported field.
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9. From the Properties windows, click the Field Statistics to view the statistics for the PAY_DAYS field. When prompted, calculate the statistics for all fields.
The company’s policy is to pay all suppliers within 25 - 35 days of receipt of the invoice.
l The minimum number of days is -6 and there are 6 negative records (i.e., payments made before the stated invoice date).
l There are 6 payments with value 0 (i.e., orders being paid on the invoice date).
l The maximum is 59 days, exceeding the standard terms.
l The average is 27 days showing most invoices are paid within policy.
10. From the Properties window, click Data to return to the database.
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Extracting all Payments Outside the Policy Range
Extract all payments made outside the company’s stated policy of 25 - 35 days.
1. On the Analysis tab, in the Extract group, click Direct.
2. In the File name field, enter Payment Days Outside Policy Range.
3. Click the Equation Editor button.
The Equation Editor appears and is used to enter the required equation.
4. Enter the expression .NOT. @Between(PAY_DAYS,25,35).
l @Between(VALUE,MIN,MAX) - tests for VALUE between (and including) MIN and MAX.
l .NOT. - reverses an expression, items where VALUE is not between MIN and MAX.
5. From the toolbar, click the Validate and Exit button to check the syntax and exit the Equation Editor.
6. In the Records to extract area, accept the default selection of the All option to extract the records from the whole database.
7. Click OK to run the extraction.
8. Double-click on the PAY_DAYS field name to sequence in ascending order. Double-click again to index in descending order. The index order will be displayed in the Indices area on the Properties window. Alternatively, from the Data tab, in the Order group, click Index.
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9. Specify the index order as PAY_DAYS /Descending.
l There are 85 payments totaling $3,010,687.40.
l There are 68 payments made before 25 days.
l There are 17 payments delayed more than 35 days.
10. Close the Payment days outside policy range database.
Conclusion
Several early payments have been authorized by HMV and VST to suppliers M100, C202 andW007. The relationship between authorizers HMV and VST and these suppliers should be investigated. Late payments may also be investigated.
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Exercise 3L: Payments to Unauthorized Suppliers
Objective:
To test the validity of payments to authorized suppliers.
Exercise Description:
It is suspected that there are payments to unauthorized suppliers.
The transactions will be matched to the Authorized Supplier-Address database using the Join Databases task in IDEA. The History log will specify the number of unmatched records (i.e., a payment for which there is no match in the supplier’s database).
IDEA Functionality Covered:
l Matching databases using the Join Databases task
l Selecting the correct primary database for joining
l Determining the number of matched or unmatched transactions
l Using the @IsBlank() function
The common key by which the databases are to be joined is the SUPPNO field in each database. Verify that the field is the same type in each database.
Verifying a Common Key
1. Ensure Accounts Payable is the active database and the Data property is selected in the Properties window.
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2. Double-click over the Database window to open the Field Manipulation dialog box and to display the field layout.
Note that the field SUPPNO is a Character field.
3. Click Cancel.
4. Open the Authorized Supplier-Address database.
5. Double-click over the Database window to open the Field Manipulation dialog and to display the field layout.
Note that the field SUPPNO is a Character field.
6. Click Cancel.
Joining the Databases
1. Make Accounts Payable the active database.
2. On the Analysis tab, in the Relate group, click Join.
The Join Databases dialog box appears with the details of the Primary database in the top section.
It is possible to enter Criteria for the Primary database (i.e., only a range of suppliers), if required. Do not enter criteria for this test.
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3. To specify the Secondary database, click Select. The Select Database dialog box appears. Select the Authorized Suppliers-Address database and then click OK.
4. Change the File name in the lower section of the Join Databases dialog box to Supplier Verification.
5. To specify the common match key, click Match to display the Match Key Fields dialog box.
6. Click the Primary text box and select SUPPNO from the list of fields. Note the Order text box and accept the default, Ascending. Click the Secondary text box and select SUPPNO from the list of fields. Click OK.
7. There are five join options at the bottom of the screen. Select the All records in primary file option.
All records in primary file is selected as:
l There are multiple payments for each supplier.
l The risk is that payments are to unauthorized suppliers, so we are not interested in suppliers with no payments (the All records in both files option).
The Join Databases dialog box should appear as in the screen below.
8. Click the OK button to join the selected databases. View the resultant database.
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9. From the Properties window, click History and then locate and expand the section for the Join Databases task.
There are 999 records in the file and that there are 9 Unmatched Primary records - these are payments to unauthorized suppliers.
Reconcile the control totals for AMOUNT and TOT_PREV_YR. For the AMOUNT field, we have already reconciled based on the above history. However, to perform this reconciliation for the TOT_PREV_YR we will have to summarize on SUPPNO.
10. On the Analysis tab, in the Categorize group, click Summarization.
11. Select SUPPNO on Fields to summarize.
12. Do not select any fields under Numeric field to total.
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13. Click Fields and select TOT_PREV_YR as a field to be included. Click OK.
14. Name the output database Supplier Analysis - Control Total and then click OK to run the task.
The control total is 29,720,396.11. This does not reconcile to the original control total of 30,202,660.57. The difference between the two is 482,264.46. Analysis is required to determine why we have this difference.
15. Make Authorized Supplier - Address the active database.
16. On the Analysis tab, in the Explore group, click Duplicate Key and then Detection.
17. Accept the default selection of the Output Duplicate Records option and enter the file name Authorized Supplier Duplicate Key.
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18. Click Key and use the SUPPNO field, as this was the field used during the Join Databases task. Click OK.
19. Click OK to run the Duplicate Key Detection task.
Two suppliers have been assigned the Supplier Number of W007; the SUPPNO was not a unique key. One of these suppliers has an amount of 482,264.46, which reconciles to the difference identified above. As the key was not unique, IDEA dropped this supplier during the join. Inquiries should be submitted to the management of Bright IDEAs as to which supplier is the correct one. We should communicate this weakness in managing supplier numbers to the client; as the supplier number should be unique.
The client has informed the audit team that the Witch Products supplier had a balance in previous years, but in the current year they do not have a balance, as they are no longer in business. Therefore, the Witch Products account should be ignored for this and other analyses. However, the auditor will need follow-up to corroborate the client’s explanation.
20. Close all databases.
21. Open the Supplier Verification database.
22. Extract all unmatched records using methods explained in earlier exercises and using the following equation: @IsBlank(SUPPNO1).
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23. Name the extraction database file, Payments to Unauthorized Suppliers.
All such payments are to supplier M100 and total $517,834.91. There is one payment with no supplier number.
24. Close the Payments to Unauthorized Suppliers database.
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Exercise 3M: Analyzing Payments by Supplier
Objective:
To analyze payments by supplier to identify large movements.
Exercise Description:
Using the Supplier Verification database, summarize (or total) the AMOUNT field for each Supplier (i.e., SUPPNO). Then identify suppliers where the total payments have either increased or decreased by over 25% in the past year. Create Editable fields to mark items for investigation and to add comments.
IDEA Functionality Covered:
l Summarizing data
l Using the @Abs() function
l Adding and using Editable fields
To analyze the payments by supplier:
1. Ensure Supplier Verification is the active database and the Data property is selected in the Properties window.
2. On the Analysis tab, in the Categorize group, click Summarization.
3. Consider whether Quick Summarization should be used.
The Use Quick Summarization option can be used as there is only one field in the key (i.e., SUPPNO). For this exercise, we will not use this option.
4. In the Fields to summarize area, select the SUPPNO field.
5. In the Numeric fields to total box, select the AMOUNT field. Do not select the TOT_PREV_YR field to total; this will be selected as additional information.
6. Click Fields and select the following additional fields to be included: PAYEE, SUPPNAME, TOT_PREV_YR.
7. Click OK on the Fields dialog box.
8. Accept the option to Create database, but do not select Create result (i.e., report). Do not enter any Criteria for the test.
9. Enter the File name for the output database as Payments by Supplier and then click OK.
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10. View the resultant database and note the NO_OF_RECS field (i.e., number of payments per supplier).
11. Sequence the database in descending AMOUNT_SUM order. Increase the column width if necessary by dragging the column title delimiter.
There should be 49 records totaling $34,145,300.89.
12. Extract all suppliers where the total payments have increased or decreased by over 25% during the past year. Select the Direct Extraction task and change the file name to Large Movements. Use the following extraction equation:
@Abs(AMOUNT_SUM - TOT_PREV_YR) * 100 / TOT_PREV_YR > 25
13. Click the Validate and Exit button.
@Abs() ignores the negative sign. Therefore, @Abs(expression) > 25 will identify both increase and decrease greater than 25%.
When testing items, it is often useful to manually mark items as correct/incorrect or to add comments to the database. Although the original data cannot be modified, it is possible to add Editable fields to the database.
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14. In the Direct Extraction dialog box, click Create Fields. Add the following fields:
NAME TYPE LEN DEC PARAMETER DESCRIPTION
TEST Multistate 1 -1 Yes, No, N/A
COMMENT Editable Character 100 " " Additional Comments
15. Click OK in the Create Fields dialog box. Click OK in the Direct Extraction dialog box.
l There should be 14 suppliers totaling $12,130,760.68 whose turnover is more than 25% different to the previous year.
l The TEST and COMMENT fields are empty. However, these are Editable fields.
l Further to investigation, it is discovered that the following suppliers may need investigation: F123 and F130.
The following do not require further investigation: M025, R025, andW007.
16. Click in the TEST field for each of these suppliers and note how the entry changes from empty, to a check mark to an "x" to finally a question mark.
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17. For suppliers F123 and F130, enter Investigate reason for large increase in payments as a comment.
18. View the History log and confirm that all modifications are recorded.
19. Close all databases.
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3.7 Audit Findings The following audit findings should be reported to the client.
The analysis of payment values showed a significant number of items just below the $80,000 authorization level. Further investigation found:
l 20 payments with CASH in the name
l 5 round sum payments of $75,000, with 3 being potential duplicates
l Zero-value payments
l 399 payments authorized by HMV, many above the $20,000 limit applicable in this case
l 77 payments were made on a Sunday and again are mainly authorized by HMV
l There are 5 missing checks: 701805, 701997, 701998, 701999, and 702000.
l There are 85 days on which no payments were processed:
l 63 payments were made within 25 days of the invoice date, which is quicker than the company policy
l 17 payments were delayed more than 35 days
l 6 payments were made before the invoices were entered into the system
l Payments were made to a supplier not on the authorized supplier list.
l Supplier numbers should be managed to ensure proper referential integrity and completeness.
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Section 4
Inventory Analysis
4.1 Introduction What are the risks associated with Inventory that could be addressed by computer assisted audit procedures? And what tests can help address those risks? This section summarizes the business and audit risks that arise in the context of Inventory and the potential tests that can be used to address those risks.
4.1.1 Potential Risks
The following table identifies the key risks, explains the business and audit implication of each risk and the audit objectives that could be addressed by audit tests.
RISK IMPLICATIONS AUDIT OBJECTIVES
1 Inventory is not correctly recorded.
Management will not have accurate information to manage inventory (i.e., ordering) effectively.
Inventory could be materially misstated on the financial statements
Accuracy, Existence, Validity
2 Inventory management reports have inadequate supporting information.
Management may identify issues but may not be able to "drill down" and identify the root cause of issues. This, in turn, hampers management’s ability to make effective decisions.
Existence, Validity,
Valuation
3 Quantity of inventory is not maintained within the specified range.
If too much inventory is maintained then there is an increased risk of obsolescence and additional storage costs (i.e., for keeping the inventory). If too little inventory is maintained, then the company will not be able to meet the customer demand.
Valuation
4 Obsolete inventory items are not identified
Management will not be able to make proper decisions on getting rid of obsolete items.
From an audit point of view, obsolete items may need to be revalued if the market price is less than their cost.
Valuation
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RISK IMPLICATIONS AUDIT OBJECTIVES
5 Differences between the physical inventory and the inventory on the system are not identified.
Management will not have reliable information to make inventory management decisions. Also, it may have a problem in terms of identifying shrinkage.
From an audit perspective, the inventory could be materially misstated on the financial statements.
Existence, Validity
6 Items are not recorded in the correct period.
Financial statements, i.e., inventory and cost of goods sold, could be materially misstated.
Cut-off
7 Gaps in sequentially numbered documents are not accounted for.
Inventory tags, transaction numbers and other information may be missing and not accounted for. This would undermine the control that is provided by inventory tags.
Completeness
4.1.2 Potential Tests
The following tests are suggested when analyzing an inventory system. However, the exact tests carried out for a client will depend upon the system used and the data available. Common tests include:
Mechanical Accuracy and Valuation
l Total the file, providing sub-totals of the categories of inventory.
l Re-perform any calculations involved in arriving at the final stock quantities and values.
l Re-perform material and labor cost calculations on assembled items.
Analysis
l Age inventory by date of receipt.
l Compute the number of months each inventory item is held based on either sales or purchases. Produce a summary of this information.
l Stratify balances by value bands.
l Analyze gross profit.
l Analyze price adjustment transactions.
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Exception Tests - Existence and Valuation
l Identify and total inventory held longer than maximum and minimum inventory levels.
l Identify and total obsolete or damaged inventory (identified as such in the database).
l Identify balances greater than a reasonable usage period that are probably obsolete.
l Identify items past their shelf life (if a sell by date or bought date is present on the system).
l Identify any items with excessive or negligible selling or cost prices.
l Identify differences arising from physical stock counts.
l Test for movements with dates or reference numbers not in the correct period (cut- off).
l Identify balances that include unusual items (i.e., adjustments).
l Identify work in progress that has been open for an unreasonable period.
l Identify inventory acquired from group companies.
Gaps and Duplicates
l Test for missing inventory ticket numbers.
l Test for missing transaction numbers.
l Identify duplicate inventory items.
Matching and Comparison Tests
l Compare files at two dates to identify new or deleted inventory lines or to identify significant fluctuations in cost or selling price.
l Compare cost and selling price and identify items where cost exceeds net realizable value.
l Compare holdings and inventory turnover per product between stores.
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4.2 Case Scenario: Inventory The Chief Financial Officer of Bright IDEAs Inc. has called to say he has identified some problems with the inventory system and would like you to help analyze the extent of these problems. Although only 36 items are identified as obsolete on the system, he thinks there are considerably more obsolete lines that need clearing out. They are also suffering from frequent stockouts and he is convinced that their margin analysis is not accurate and that re-order levels are incorrect.
He supplies you with the following information:
l There are 767 product lines in the database.
l Bright IDEAs Inc. has three depots based in Ottawa, Toronto and Quebec.
l The file is as of December 31, 2015.
You agree on the following:
l To check the system calculation of the obsolescence provision, to prepare an analysis of inventory and usage, and to suggest an appropriate provision.
l To identify items with incorrect re-order levels.
l To perform an analysis of profit margins on different lines.
There is normally a master or balances file that contains details of inventory holdings at a particular date. Costs may be held in a separate file. The transaction history can be particularly useful although file sizes are often quite large. Selling prices normally must be picked up from a separate file.
Retailers often have point-of-sale systems that may collect large volumes of useful data that IDEA can analyze.
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4.3 Obtaining the Data The data file used for this exercise will be: Inventory on hand at December 31, 2015.
4.3.1 Explanation of the ASCII Delimited File Format
ASCII delimited files are a common format of variable length file where each field is only long enough to contain the data stored within it. This is an advantage with text fields such as names, addresses, narratives, etc.
For software to use such data files, there is a separator (a special character) at the end of each field within the record. Additionally, text fields may be enclosed (encapsulated) with another character, typically quotation marks (" ").
The separator is often a comma (,) and such files are known as comma separated value (CSV). However, the separator could be another ASCII character, for example a colon (:) or a semi-colon (;). These files are referred to as ASCII Delimited (comma separated files are also often referred to as ASCII Delimited).
There must also be a record delimiter at the end of each record, typically the carriage return (<CR>) and line feed (<LF>) characters, or sometimes only the line feed character.
When this type of file is imported, a fixed length database is created within IDEA.
Example of an ASCII Delimited - CSV:
"6776",1865,1,190,"04-02-2006" <CR><LF>
"6714",1865,1,97,"04-02-2006"<CR><LF>
"11905",2366,10,9,"25-02-2006"<CR><LF>
"7555",4352,1,79,"03-03-2006"<CR><LF>
"4547",4815,5,17,"03-03-2006"<CR><LF>
"6344",4815,1,7,"03-03-2006"<CR><LF>
"151",2366,1,3,"29-03-2006"<CR><LF>
"145",2366,1,1,"05-08-2006"<CR><LF>
"206",2366,1,2,"29-03-2006"<CR><LF>
"207",2366,1,3,"29-03-2006"<CR><LF>
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4.3.2 Requesting Data Files for Analysis Purposes
Please supply the following data from the Inventory system in ASCII Delimited format:
FIELD NAME TYPE DEC DESCRIPTION
PRODCODE Numeric 0 Product Code
DEPOT Character Depot name
QTY Numeric 0 Quantity on hand
AV_COST Numeric 3 Average cost for item
TOTALCOST Numeric 3 QTY*AV_COST
OBSOLETE Character Obsolete = "Y"
MAX Numeric 0 Maximum inventory level on hand
MIN Numeric 0 Minimum inventory level on hand
DELQTY Numeric 0 Last delivery quantity
ORDERNO Numeric 0 Last delivery order number
DELDATED Date Date of last delivery YYYYMMDD
CURSELLPRI Numeric 2 Current selling price
CUREFFDATE Date Date selling price effective YYYYMMDD
PREVSELLPRI Numeric 2 Previous selling price
USAGE Numeric 0 Sales quantity in current year
PREVCOST Numeric 3 Unit cost of last purchase
Data is required for the following period: December 31, 2015
Please also supply the following control totals for reconciliation purposes:
l Total cost of inventory on hand at December 31, 2015.
l Number of transactions supplied, i.e., product lines.
l Record definition to support the data file provided.
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4.4 Work Plan From the available/possible tests the audit manager decides on the appropriate tests and prepares the following Work Plan for you to complete.
Objective:
To check the system calculation of obsolesce provision, to prepare an analysis of inventory and usage, and to suggest an appropriate provision.
AUDIT PROCEDURES WORK DONE BY (INITIALS)
EXTENT OF
TESTING
1 Obtain the data files from the client and load into the PC.
Load IDEA and set a Working Folder for the Inventory Audit for Bright IDEAs Inc.
2 Agree the total value of inventory:
a) Import the inventory data;
b) Check the total;
c) Compute field statistics; and
d) Reconcile control totals.
3 Consider the adequacy and accuracy of inventory provisions:
a) Test the accuracy of the provisioning report;
b) Compute the usage figures and ratios for all stock lines and calculate an alternative provision; and
c) Analyze the provision by depot.
4 Consider the accuracy of the re-ordering system and test for items that are not being ordered frequently enough.
5 Analyze profit margins and pricing by sales bands and identify items with inappropriate or questionable pricing:
a) Items with no selling price; and
b) Major pricing movements.
6 Create a Stratified Random Sample of the current inventory.
7 Create custom dashboards using the Visualize task to view the data.
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Exercise 4A: Project Setup
Objective:
To be able to start an assignment including loading data, running IDEA and creating a project for the client within IDEA.
Exercise Description:
This exercise covers copying data into a folder, loading IDEA and creating a new project for the client.
IDEA Functionality Covered:
l Creating a project
l Entering project properties
Required Data Files:
The following data files are provided with this workbook:
l Inventory 2015.asc – Inventory on hand at December 31, 2015
Accessing IDEA
1. From the Windows Start menu (e.g., Windows 10), navigate to and expand the IDEA folder.
2. Click IDEA.
Creating the Inventory Audit Project
To facilitate housekeeping, it is recommended that a separate project be used for each audit, investigation or analysis.
All information relating to this analysis, including data files, equations, views or report definitions, import definitions, etc. may be stored in the project.
This exercise will explain how to create a project and enter project information that will be printed on all reports.
Exercise
Section 4: Inventory Analysis
Once a project is set, it remains the active folder until changed.
1. To create a new project, on the Home tab, in the Projects group, click New.
The New Project dialog box appears.
To create a new project in pre-IDEA 11.2 versions, on the Home tab, in the Projects group, click Create.
2. Select Managed project and enter Inventory Audit as the project name.
3. Click OK.
This will create a new folder:
C:\Users\[UserID]\Documents\My IDEA Documents\IDEA Projects\Inventory Audit
Inventory Audit will become the active project, closing the previously active project.
4. On the Home tab, in the Projects group, click Properties.
The Project Properties dialog box appears.
5. Enter the following:
l Report name: Inventory Audit
l Report period: Jan 1, 2015 to Dec 31, 2015
The project properties will be stored in a file called Client.inf in the project folder.
Loading Data
The following data file is provided with the workbook and is required for the Inventory Audit project.
l Inventory 2015.asc – Inventory on hand at December 31, 2015
Use either of the following methods to add the data file to the project:
l Use IDEA to add the data file to the project:
1. In IDEA, click the Library tab.
2. From the Current Project Library, right-click Source Files and click Add File....
3. Navigate to and select the required file.
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l Use Windows Explorer to manually copy the data file to the project:
C:\Users\[UserID]\My IDEA Documents\IDEA Projects\Inventory Audit\Source Files.ILB
This is the default location within a project to store any source files.
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Exercise 4B: Importing the Inventory File
Objective:
To import the data file for testing.
Exercise Description:
The year-end inventory file has been provided in a format known as ASCII Delimited.
In order to load this file it is necessary to define its format to IDEA. The Import Assistant will guide you through the steps to define and import the file for testing.
IDEA Functionality Covered:
l Access the Import Assistant
l Build a record definition for an ASCII Delimited file
l Save the definition
l Import the database into IDEA
l Select a numeric control amount field for the database
Required Data File:
You have been provided with an ASCII Delimited file:
l Inventory 2015.asc – the Inventory file as at December 31, 2015.
To import the data file for testing:
1. On the Home tab, in the Source Data group, click Import.
To access the Import Assistant in pre-IDEA 11.2 versions, on the Home tab, in the Import group, click Desktop.
2. Select Text from the list and click the Browse button adjacent to the File name field.
3. In the Select File dialog box, select Inventory 2015.asc and click Open.
4. In the Import Assistant, click Next.
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5. On the Import Assistant - File Type screen, ensure Delimited is selected as the correct file type.
6. View the data and determine the field separator and the record delimiter.
7. Click Next.
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8. The Import Assistant will try to determine the Field Separators and Text Encapsulators (if any) for the file.
Do not select the First visible row is field names option, and click Next to proceed.
ASCII Delimited files can contain field names as the first row of the file. To use these as field names, select the First visible row is field names option.
The Import Assistant - Field Details screen appears.
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9. Click on each field heading in turn and using the record definition, define the field details.
FIELD NAME TYPE DEC DESCRIPTION
PRODCODE Numeric 0 Product Code
DEPOT Character Depot name
QTY Numeric 0 Quantity on hand
AV_COST Numeric 3 Average cost for item
TOTALCOST Numeric 3 QTY*AV_COST
OBSOLETE Character Obsolete = "Y"
MAX Numeric 0 Maximum inventory level on hand
MIN Numeric 0 Minimum inventory level on hand
DELQTY Numeric 0 Last delivery quantity
ORDERNO Numeric 0 Last delivery order number
DELDATED Date Date of last delivery YYYYMMDD
CURSELLPRI Numeric 2 Current selling price
CUREFFDATE Date Date selling price effective YYYYMMDD
PREVSELLPRI Numeric 2 Previous selling price
USAGE Numeric 0 Sales quantity in current year
PREVCOST Numeric 3 Unit cost of last purchase
10. Enter the correct field name in the Field name box. The Import Assistant suggests the file type for that field in the Type box. If this is incorrect then change it to the file type specified in the record definition.
11. Enter the description of the field in the Description box.
12. If the field is a Numeric field with decimals, it is necessary to specify whether the decimals are implied. However, in this database if there are decimals, the decimal place is stored in the number, therefore the Implied decimals box must not be selected. Specify the number of decimals in the Number of decimals box.
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13. Define the Date fields as type Date, i.e., change the Character to Date in the Type box. A mask must be defined for the Date fields. Click the Date Mask box and enter the appropriate date format (i.e., YYYYMMDD).
14. Click Next.
15. The Import Assistant – Create Fields screen appears. Create Fields allows you to add fields in the imported file. This can be done during the import or at any time while using IDEA. For this exercise, no fields will be added.
16. Click Next.
The Import Assistant - Import Criteria screen appears. We will not be entering criteria for this example.
17. Click Next.
18. The definition will automatically be saved in the Project Import Definitions.ILB. IDEA will give the definition the same name as the source file. Click the Browse button adjacent to the Save record definition as box if you want to change the name to something other than Inventory2015.rdf.
19. Select the Generate field statistics option and enter Inventory at Dec 31 2015 in the Database name box.
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20. Click Finish to import the file into IDEA.
21. Click the Control Total link in the Properties window and select the TOTALCOST field and then click OK.
The control total of 626,963.915 will appear beside the Control Total link in the Properties window.
There are 767 records as can be seen on the status bar at the bottom of the screen.
You are now ready to verify that the data has been imported correctly and to commence testing.
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Exercise 4C: Verifying that the Database Has Been Correctly Imported
Objective:
To prove that data has been imported correctly and verify the accuracy of the inventory report totals.
Exercise Description:
View the Field Statistics for the Numeric fields in the Inventory at Dec 31 2015 database.
The numeric statistics will be used for:
l Agreeing totals
l Getting a general understanding of the ranges of values in the database
l Highlighting potential errors/areas of weakness for focusing on subsequent investigations
IDEA Functionality Covered:
l Viewing Field Statistics
l Printing Field Statistics
To verify that the database has been correctly imported:
1. From the Properties window, click Field Statistics.
Field Statistics are displayed for the Numeric fields in the database.
The Field Statistics are available instantly as they were generated when the database was imported. Field Statistics are also available for Date fields. These will be reviewed in the following exercise.
Exercise
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2. Study the Field Statistics for all Numeric fields, but especially, the QTY and TOTALCOST fields. Note the Net Value, Minimum Value, Maximum Value, # of Zero Items and # of Negative Records field statistics.
Please check your results against the figures below.
Field: QTY
STATISTIC VALUE COMMENT
Net Value 214,954
Absolute Value 214,984
# of Records 767
# of Zero Items 68
Positive Value 214,969
Negative Value -15
# of Positive Records 696
# of Negative Records 3 These should be identified
# of Data Errors 0
# of Valid Values 767
Average Value 280.25
Minimum Value -8 Should not be any negative quantities
Maximum Value 10,800
Record # of Min 202
Record # of Max 576
Sample Std Dev 653.53
Sample Variance 427,098.71
Field: TOTALCOST
STATISTIC VALUE COMMENT
Net Value 626,963.915
Absolute Value 627,159.615
# of Records 767
# of Zero Items 69 Different number for zero-quantity
Positive Value 627,061.765
Negative Value -97.850
# of Positive Records 695
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STATISTIC VALUE COMMENT
# of Negative Records 3
# of Data Errors 0
# of Valid Values 767
Average Value 817.424
Minimum Value -67.000
Maximum Value 70,555.641
Record # of Min 90
Record # of Max 384
Sample Std Dev 3,863.12
Sample Variance 14,923,698.38
3. From the toolbar, click the Export button to export the field statistics for the QTY and TOTALCOST fields to keep proof of reconciliation.
4. The Field Statistics reports should be filed with the audit documentation as proof of reconciliation.
5. From the Properties window, click Data to display the database.
Conclusion
The file has been imported correctly and the client’s reports reconcile correctly. However, there are some negative items and we should identify these and report them to the client.
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Exercise 4D: Identifying Obsolete Inventory Items
Objective:
l To check the client’s calculation of the obsolescence report.
l To identify obsolete inventory and calculate potential provisions.
l To identify any items with negative quantities or costs.
Exercise Description:
There are obsolete inventory items on the database, denoted by a "Y" in the OBSOLETE field.
l View the Date Field Statistics for the last delivery date to determine the age of inventory items.
l Confirm the client’s calculation of obsolete items by reference to the items marked as obsolete. At the same time identify the negative value and quantity items.
l Extract items with inventory on hand and perform a calculation of usage.
l Analyze the inventory by usage and then calculate a provision based on this.
l Analyze the provision by depot.
l Identify any major items for detailed investigation.
IDEA Functionality Covered:
l View and analyze Date Field Statistics
l Perform calculations by adding Virtual fields
l Perform multiple extractions
l Copy, paste, and edit similar extraction equations
l Perform summarizations
To view the Field Statistics for the DELDATED (date of last delivery) field to determine the age of the oldest stock items:
1. In the Properties window, click Field Statistics.
2. In the Field Type box, select Date.
Exercise
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3. Note the Earliest Date, Latest Date, and # of Zero Items (i.e., missing dates) statistics for DELDATED.
Check your results against the figures below.
Field: DELDATED
STATISTIC VALUE COMMENT
# OF Zero Items 0 Items with no delivery date
Earliest Date 9/21/2001 Old inventory items to be investigated
Latest Date 12/18/2015
The next test to carry out is to prove the client’s obsolescence provision report.
4. From the Properties window, click Data.
5. On the Analysis tab, in the Extract group, click Direct.
6. Change the file name to Obsolete Inventory Items.
7. To enter the equation, click the Equation Editor button . The Equation Editor appears and is used to enter the required equation to identify items flagged as obsolete.
8. Enter the expression: OBSOLETE = "Y" as follows:
l Double-click the OBSOLETE field name to add it to the equation.
l Click the = button.
l Click the "" button.
l Enter Y (in uppercase) within the quotes as below
9. From the toolbar, click the Validate button to check the syntax. If a syntax error occurs in your equation, correct the expression and recheck the syntax.
Click the Validate and Exit button but do not run the extraction yet.
10. Multiple extractions (up to 50) can be carried out with a single pass through the database. We will use this feature to report the negative items. Click on the next row in the spreadsheet area of the Direct Extraction dialog box. Enter the File name: Negative Quantities.
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11. Click the Equation Editor button and enter the following equation: QTY < 0.
l Insert QTY into the equation by double-clicking the field name.
l Type in <.
l Type in 0.
l Click the Validate and Exit button but do not run the extractions yet.
If equations are similar, they can be copied, pasted and edited to save time.
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12. Enter the information for the third extraction:
a. Double-click to select QTY < 0 from the Direct Extraction box.
b. Press CTRL + C (to copy to the clipboard).
c. Click on the Criteria text box for the third equation.
d. Press CTRL + V (to paste from the clipboard).
e. Click on the Equation Editor button to edit the equation.
f. Replace the QTY field with TOTALCOST.
g. Click the Validate Equation button to check the syntax (if a syntax error occurs in your equation, correct the equation and recheck the syntax).
h. Click the Validate and Exit button.
i. Change the file name to Negative Cost Items.
13. Click OK to run all three extractions with a single pass through the database.
l The resultant databases will be created and the Obsolete Inventory Items database will be displayed.
l There should be 34 items totaling $7,644.270 that matches the client’s report.
l Open the Negative Cost Items database from the File Explorer. It should contain 3 items totaling $-97.850.
l Open and inspect the Negative Quantities database. There should be 3 items totaling $-30.850.
14. Close all databases.
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Conclusion
The client’s report is mathematically accurate regarding the totals for those items flagged as obsolete. Whether other items should be flagged as obsolete will be tested next. The negative items have now been identified and can be given to the client for correction.
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Exercise 4E: Calculating Usage Ratios and Suggest Obsolescence Provision
Objectives:
To analyze inventory usage and calculate provision.
Exercise Description:
l Calculate the monthly value of inventory on hand, based on the usage over the past 12 months using a Virtual (Calculated) field.
l View the Field Statistics of the calculated fields.
l Analyze the inventory into bands of six months using Stratification.
l Produce a report, including a chart of the Stratification results and optionally email results to the Chief Financial Officer.
IDEA Functionality Covered:
l Add a Virtual field to calculate the number of months of inventory
l Use the following@Functions:
l @Age
l @If
l Save an equation
l View Field Statistics
l Perform a Stratification, including creating a detailed stratification database
l View an extraction preview of exceptions
l Create a report, including a graph of the Stratification results
l Print and export the results to a file or send via email
Calculating Months of Inventory
1. Open Inventory at Dec 31 2015 as the active database.
Performing a calculation and storing the result in a new column is called adding a Virtual Field and is carried out through the Field Manipulation task.
Exercise
Section 4: Inventory Analysis
2. Double-click in the Database window to open the Field Manipulation dialog box.
3. On the Field Manipulation dialog box, click Add and enter the following details:
l Field Name: MONTHS
l Field Type: Virtual Numeric
l Field Length: Do not enter
l Decimals: 2
l Description: Months of inventory on hand
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4. Click in the Parameter cell to load the Equation Editor and enter the following equation:
@If(USAGE = 0, @Age("20151231",DELDATED)/30 , QTY/(USAGE/12))
a. In the @Functions area, expand the Conditional category. This displays the two conditional @Functions, @Compif and@If.
b. Click @If and read the help and example in the information area.
c. Double-click to select@If, inserting it into the Equation area, or click Insert Function.
d. Select the field USAGE by double-clicking the field name.
e. Continue selecting and typing the relevant items to build the equation. Ensure parentheses are in the correct position.
f. The equation should appear as in the screen below.
The following @Functions are used in this equation:
l @Age: This computes the number of days between two dates, in this case, the number of days between the delivery date and the year-end. An assumption has been made that there are 30 days to a month to give an approximate number of months figure.
l @If: This performs a test, in this case whether there is any usage. If the equation USAGE = 0 is true then the value after the first comma is used, otherwise the value after the second comma is used.
For more help on the @Functions, review the associated information for the specific @Function in the Equation Editor.
In the above equation:
l If inventory usage is 0 (i.e., the true condition in @If), the number of months of inventory is calculated as the number of months since the last
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delivery date, i.e., @Age ("20151231",DELDATED)/30.
l If there has been usage (i.e., USAGE <> 0), the months of inventory on hand is calculated as a ratio of the quantity on hand to the monthly usage, i.e., QTY/(USAGE/12).
As this is a complex equation that may be used again or may require editing, it should be saved. Equations may be saved in any folder however it is recommended that all files relating to the Inventory Audit are stored in the project folder under the Equations.ILB folder.
5. From the toolbar, click the Save Equation button . When the Save As dialog box appears, enter the File name: Months of Inventory on Hand.
6. Click Save. All equations are save with and EQX file extension.
7. Click the Validate and Exit button, returning to the Field Manipulation dialog box.
8. Click OK and then Yes to add the field to the database.
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9. View the resultant field by scrolling to the right of the display. The new field is added to the end (last field) of the database. Note the different color of the values in the field. It indicates that it is a calculated field and not an original imported field.
In discussion with the client, it becomes apparent that the shelf life of most Bright IDEA’s products is only a few months and anything beyond six months’ use is likely to be obsolete.
10. To view equations that have been saved IDEA keeps all the information in a library. You can access the library by clicking the Library tab at the bottom of the File Explorer window.
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11. The Library tab contains a list of all the directories in which IDEA stores different type of information, such as equations, macros, exported documents, record definitions, custom functions, import definitions and source files. When the equation was saved it was saved automatically to the Equations folder under the Current Project Library:
12. If you wish to have any item in the Current Project Library available to all projects, you can right click on the item and select Copy to the Local Library. You can also copy the item to another project in the same manner by copying to Another Project.
Generating Field Statistics
To produce the Field Statistics for the MONTHS field:
1. From the Properties window, click Field Statistics.
A message dialog box appears that asks whether you want to create statistics for all fields without statistics.
2. Click Yes.
Field Statistics will be recalculated for all invalid or new fields. View the statistics for the MONTHS field.
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3. Note the Average Value, Minimum Value, andMaximum Value.
STATISTIC VALUE COMMENT
Average Value 9.13
Minimum Value -1.55 Negative Items
Maximum Value 737.14
The average may be misleading as it may be skewed by a few very high values of usage. Hence, it is often better to perform the Stratification task to profile the data. This is carried out in the next exercise.
4. To return to the database, from the Properties window, click Data.
Stratifying the Inventory into Bands
1. Ensure Inventory at Dec 31 2015 is the active database and the Data property is selected in the Properties window.
2. On the Analysis tab, in the Categorize group, click Stratification.
The Stratification dialog box appears.
3. Select MONTHS in both the Field to stratify and Fields to total on list boxes.
l Click on the Increment text box and enter 6 (6.00 will be displayed).
l Click on the first row of the spreadsheet area – the Lower Limit box will automatically fill with the lowest value in the field to be stratified.
l Double-click on the Lower Limit box in the first row; the value will be highlighted so you can type over it to change it. Change the value to 0 (0.00 will be displayed).
l Click in the Upper Limit box on the first row of the spreadsheet area which will fill in your first increment.
l Highlight the next 5 rows of the spreadsheet area and note how they fill with the increment, this should take the range up to 36.
4. Select the Create database and the Create result options.
5. Select the Include stratum intervals option.
6. Change the File names for both the database and the result to Inventory Usage Aging.
7. Click Fields and select only PRODCODE, DEPOT, TOTALCOST, andMONTHS.
To select or deselect a field, click the field name.
8. Click OK in the Fields dialog box.
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9. Click OK in the Stratification dialog box.
10. Note the number of records in each stratum and the Upper limit exceptions (i.e., items with greater than or equal to 36months inventory in hand).
11. View the items with greater than or equal to 36months inventory in hand by double-clicking over the Upper limit exceptions results and selecting Display Records.
12. In the Preview Database window, click Save to save these items to a new database. Enter Upper Limit Exceptions as the file name and click OK.
Once you have inspected the records of the Upper Limit Exceptions database, click the Inventory at Dec 31 2015 database tab to return to the Stratification results.
13. From the Results toolbar, click the Chart button. From the chart toolbar, click the 3D/2D button.
14. If applicable, from the chart toolbar, click the Print button.
15. Return to viewing the Stratification analysis by clicking the Chart icon on the Results toolbar.
16. From the Properties window, click Data to return to viewing the database.
17. Open the Inventory Usage Aging database created as part of this exercise. View this database and note the three additional fields added to the database (i.e., STRATUM, STRAT_LOW, STRAT_HIGH).
The last two fields were added as a result of selecting the Include stratum intervals option on the Stratification dialog box.
18. Close all databases.
Conclusion
There are 611 (or 79.66) of the product lines that have less than 6 months usage. However, 4.43% (34 lines) have more than 3 years usage and these are a cause for concern.
There are also 3 items with negative usage, these should be identified and investigated.
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Exercise 4F: Calculating the Total Provisions for Inventory by Depot
Objective:
l To provide an analysis of the total cost of the obsolete inventory.
l To identify old items for checking.
l To analyze the data by depot.
Exercise Description:
Total the value of obsolete inventory by depot. The Summarization task is used to provide totals of specified Numeric fields.
Additionally, this test will identify the number of obsolete inventory products as well as the total cost by depot.
IDEA Functionality Covered:
l How to summarize (i.e., total) data by a key
l The differences between the Summarization options
Discussion with the Chief Financial Officer on the results of the Stratification task indicate that items should have an obsolescence provision calculated on the following basis:
l 6-12 months: 50%
l Over 12 months usage in stock: 100%
The whole value of an item should be used for the provision calculation, not just the surplus over 6 or 12 months usage.
The following tests have been requested:
l Include the percentage provision for each age band
l Calculate the provision cost for each band for each depot
l Summarize to calculate the total provision for each depot
Exercise
Section 4: Inventory Analysis
Including Provision in Database
1. Open and ensure that Inventory Usage Aging is selected as the active database.
2. Double-click in the Database window to access the Field Manipulation dialog box.
3. Click Add to add the following Virtual field:
l Field Name: PROV_RATE
l Field Type: Virtual Numeric
l Field Length: Do not enter
l Decimals: 2
l Description: Rate at which provision is to be calculated
4. Click in the Parameter box to load the Equation Editor and enter the following equation:
@If(STRATUM <= 1, 0, @If( STRATUM = 2, 50, 100 ))
STRATUM 0 is lower limit exceptions. STRATUM 1 is less than 6 months. STRATUM 2 is 6 to 12 months.
5. Click the Validate and Exit button, returning to the Field Manipulation dialog box.
6. Do not click OK on the Field Manipulation dialog box, as a further calculated field will be added in the next step.
Calculating the Provision (Value) by Product
1. Click Add to add the following Virtual field:
l Field Name: PROV_AMT
l Field Type: Virtual Numeric
l Field Length: Do not enter
l Decimals: 2
l Description: Provision amount
2. Click in the Parameter cell to load the Equation Editor and enter the following equation:
TOTALCOST * PROV_RATE / 100
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3. Click the Validate and Exit button, returning to the Field Manipulation dialog box.
4. Click OK and then Yes to add the two fields to the database.
5. View the resultant fields by scrolling to the right of the display. The new fields are added to the end (last fields) of the database. The data in both these fields is displayed in teal to distinguish them from original imported fields.
Calculating the Total Provision (Value) by Depot
To summarize the database on more than one field in the key (i.e., by STRATUM within DEPOT) it is necessary to use the Summarization option.
1. Ensure Inventory Usage Aging is selected as the active database and the Data property is selected in the Properties window.
2. On the Analysis tab, in the Categorize group, click Summarization.
3. In the Fields to summarize area, select DEPOT and STRATUM.
4. Select the TOTALCOST and PROV_AMT fields to total these amounts in the summarized file.
5. Select the Create database option, but do not select the Create result option.
6. Leave the Criteria box empty.
7. In the File name field, enter Proposed Provision by Depot.
8. Click OK.
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9. View the output database and note the following fields:
l DEPOT: List of depots
l STRATUM: Age band (months)
l NO_OF_RECS: Number of records band
l TOTALCOST_SUM: Cost of inventory per depot/age band
l PROV_AMT_SUM: Total provision per depot/age band
The NO_OF_RECS field is blue and underlined. This denotes an Action Field. Click the blue values to see the underlying records.
There are 4 depots listed in this database. London is not an approved depot. These records should be investigated.
Next, you are going to flag the database in the File Explorer window. A flagged database indicates that it contains significant findings. Currently, it is situated two levels down in the tree view.
10. From the File Explorer window, select the database you want to flag.
11. From the File Explorer toolbar, click the Flag File button .
The File Explorer may need to be resized in order to access the button. Alternatively, you can right-click on the database name in the File Explorer and select Flag File.
After the database is flagged, it will have a new icon and will be moved up in the tree hierarchy.
12. To remove the flag from a file, highlight the database name in the File Explorer window and click on the Flag File button again. Alternatively, you can right-click on the database name in the File Explorer and select Flag File again.
A report of the proposed obsolescence provision should now be created. Produce a report of the results with subtotals for each depot.
13. On the File tab, click Print and then Create Report. The Report Assistant will be displayed. Accept all options on the first step dialog box to create a horizontal report. Click Next.
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14. The Report Assistant – Headings step.
a. Modify the following field names by highlighting the field name in the list box on the left and typing your changes in the Text box on the bottom right.
b. TOTALCOST_SUM modify to TOTAL COST.
c. PROV_AMT_SUM modify to PROVISION AMOUNT.
d. Click Next.
15. The Report Assistant – Define Breaks step.
a. Enter the report breaks index as shown below:
l DEPOT: Ascending
l STRATUM: Ascending
b. Click Next.
16. The Report Assistant – Report Breaks step.
a. Enter or select the following options:
l Break Keys: DEPOT
l Break on this field check box: Select
l Break spacing: 3 Lines
l Fields to Total: NO_OF_RECS, TOTALCOST_SUM, PROV_AMT_SUM
b. Click Next.
17. The Report Assistant – Grand Totals step.
a. In the Fields to Total list box, select the following fields:
l NO_OF_RECS
l TOTALCOST_SUM
l PROV_AMT_SUM
b. Click Next.
18. The Report Assistant – Header/Footer step.
a. Do not print a cover page.
b. Click Finish. When prompted, preview the report created.
c. Click Yes. Zoom and navigate through the report.
19. Print the report by clicking on the Print button on the Preview window.
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20. Click OK to print the report.
21. Close all databases.
Summarization is often used to produce a list of expected unique items for verification (e.g., depots in this exercise), as well as producing totals for selected Numeric fields.
Another way to display the proposed provisions by depot is to create a Pivot Table. Pivot Tables have been used in spreadsheet software for a long time. Pivot Tables can be created easily in IDEA. The created Pivot Table becomes a Results output in the source database.
Create a Pivot Table to summarize provision and total cost amounts by depot.
1. Open the Inventory Usage Aging database.
2. On the Analysis tab, in the Categorize group, click Pivot Table.
3. A prompt will require labeling the Pivot Table results. Enter Inventory Usage Pivot Table and then click OK.
An empty Pivot Table framework appears.
4. From the Pivot Table Field List dialog box, highlight the DEPOT field name and drag it to the Drop Column Fields Here area.
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5. Highlight the STRATUM field name from the Pivot Table Field List dialog box and drag it to the Drop Row Fields Here area.
6. Highlight the PROV_AMT field from the Pivot Table Field List dialog box and drag it to the Drop Data Items Here area.
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7. Highlight the TOTALCOST field name and drag it to the Drop Data Items Here area to finish the Pivot Table.
8. Close the Pivot Table Field List dialog box.
9. From the Pivot Table toolbar, click the Send to Excel button to send the Pivot Table results to Excel. In the Send to Excel dialog box, click Save.
This exercise illustrates the interactive mode of using IDEA, i.e., the results of one test highlights problems and leads to further tests being carried out.
Conclusion
The client’s provision for obsolete items of $9,360 is clearly inadequate as the Chief Financial Officer suspected. The actual total is $152,724.42. In addition, the client may want to dispose of excess inventories. Additional reports of high value items, or particularly old items by depot, may be useful to the client but are not part of this tutorial.
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Exercise 4G: Test the Accuracy of the Automatic Reordering System
Objective:
To test the accuracy of the ordering system.
Exercise Description:
A new automatic re-ordering system has been implemented. However, it has been noted that orders are being lost due to unavailability of the required inventory.
The automatic re-ordering system has maximum and minimum ordering levels. However, these limits can be manually over-ridden. The level of obsolete inventory and unavailability of inventory point to the fact that either the limits require review or ordering levels are being incorrectly altered.
IDEA Functionality Covered:
l Use the @Between function
l Index a Database
To perform the test:
1. Open and ensure the Inventory at Dec 31 2015 database is selected as the active database.
2. On the Analysis tab, in the Extract group, click Direct.
3. Change the file name to Re-Order Level Errors.
4. To enter the equation, click the Equation Editor button. The Equation Editor will appear and is used to enter the required equation.
Exercise
Section 4: Inventory Analysis
5. Enter the expression: .NOT. @Between(QTY , MIN , MAX) as follows:
a. Click the NOT button.
b. In the Functions box, expand the Matching category.
c. Locate and double-click the function name @Between.
d. Double-click the QTY field from the lower portion of the Equation Editor dialog box.
e. From the Equation Editor toolbar, click the Comma button.
f. Double-click the MIN field from the lower portion of the Equation Editor dialog box.
g. From the Equation Editor toolbar, click the Comma button.
h. Double-click the MAX field from the lower portion of the Equation Editor dialog box.
The @Between function will be used to test for quantities outside the minimum and maximum order limits.
The @Between function has three parameters. The first is the field being tested. The second is a low limit for that field and the third is a high limit. @Between selects those items that are between the two limits. It is quite common to use @Between preceded with .NOT. to identify items that are outside the set limits.
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6. Once the equation has been entered, check the syntax by clicking the Validate button. If a syntax error occurs in your equation, correct the expression and recheck the syntax. The equation should be as in the above screen. Click the Validate and Exit button.
7. Click OK to run the extraction.
The Re-Order Level Errors database is displayed. It should contain 314 items with a total cost of $378,182.783.
This level of items outside the re-order levels confirms serious problems. There are too many items to investigate so we will prioritize by indexing.
8. Sequence the data into the most suitable order for follow-up.
9. On the Data tab, in the Order group, click Index.
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10. Choose the following sequence:
l QTY: Ascending
l MIN: Descending
11. Click OK to create the index.
The data is sequenced with the lowest quantity items with the highest minimum re-order level at the top for investigation.
Briefly inspect the report. This will be given to the client for them to investigate.
12. Click Close by right-clicking on the Re-Order Level Errors database tab.
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Exercise 4H: Analyzing Selling Prices and Margins
Objectives:
To identify items with inappropriate profit margins.
Exercise Description:
l Identify items where no current selling price has been entered
l Analyze the variance in selling price over the year and identify unusually large or small changes
l Analyze profit margins and extract negative or small margins
IDEA Functionality Covered:
l Use Criteria rather than Direct Extraction to identify items
l @If
l Stratification
Identifying Items Where No Selling Price Has Been Entered
1. Open the Inventory at Dec 31 2015 database.
In this exercise, the Criteria property in the Properties window will be used instead of performing a Direct Extraction to identify the items.
Similar to the Direct Extraction task, applying a criteria identifies records satisfying a given equation, but it does not create an output database.
2. From the Properties windows, click Criteria to access the Equation Editor.
3. Enter the following equation to identify a missing current selling price:
CURSELLPRI = 0
Exercise
Section 4: Inventory Analysis
4. Click the Validate and Exit button in the Equation Editor to complete the equation.
The criterion is applied to the database.
The number of records selected (i.e., 1/767) is displayed in the status bar at the bottom of the window. This indicates that there is one item with no selling price in a database of 767 items. This item has a quantity in inventory. The cost of this item is $24.102 as can be seen in the Properties window.
5. From the Properties window, right-click Criteria and select Clear to remove the applied criterion.
Analyzing Selling Price Movements
We decide to test whether prices are being increased or decreased and look for trends. We first calculate the percentage price movement and then perform an analysis.
1. Ensure Inventory at Dec 31 2015 is selected as the active database and the Data property is selected in the Properties window.
2. Double-click over the Database window to load the Field Manipulation dialog box.
3. Click Add to add the following Virtual field:
l Field Name: PRICE_MOV
l Field Type: Virtual Numeric
l Field Length: Do not enter
l Decimals: 2
l Description: Price movement
4. Click in the Parameter text box to load the Equation Editor and enter the following equation:
@If (PREVSELLPRI = 0, 0, (CURSELLPRI - PREVSELLPRI) * 100 / PREVSELLPRI)
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5. Click the Validate and Exit button, returning to the Field Manipulation dialog box.
6. Click OK and then Yes to add the PRICE_MOV field to the database.
7. From the Properties window, click Field Statistics.
8. Calculate the statistics when prompted.
Record the Minimum, Maximum and Average values for the PRICE_MOV field.
STATISTIC VALUE COMMENT
Minimum -53.00 At least one item has had major price cuts
Maximum 11,375.00 This is likely to be the correction for an error
Average 20.59 Client feels this is rather high
The average figure is skewed so we should prepare a profile.
9. To return to the database, from the Properties window, click Data.
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10. Stratify the PRICE_MOV into bands as follows:
a. On the Analysis tab, in the Categorize group, click Stratification.
b. In the Field to stratify list box, select PRICE_MOV.
c. In the Fields to total on, select PRICE_MOV.
d. In the Increment box, delete the default value and enter 10.
e. Enter the following stratification bands:
>=LOWER LIMIT < UPPER LIMIT
(50.00) (40.00)
(40.00) (30.00)
(30.00) (20.00)
(20.00) (10.00)
(10.00) 0.00
0.00 10.00
10.00 20.00
20.00 30.00
30.00 40.00
40.00 50.00
f. Do not select the Create Database check box.
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g. In the Result name field, enter Price Move Stratification.
h. Click OK to perform the Stratification.
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11. View the Price Move Stratification Results output.
Many items have had price increases in the range 0 - 20%.
The 7 items more than 50% (i.e., the Upper limit exceptions) and 8 items below 50%, require further investigation.
12. From the Properties windows, click Data.
13. Extract any high variances, excluding new items into a database namedMajor Price Movements. Enter the following extraction equation using the Equation Editor:
@Abs(PRICE_MOV) > 50 .AND. PREVSELLPRI <> 0
14. Run the extraction.
@Abs(PRICE_MOV) > 50 identifies increases and decreases > 50%. PREVSELLPRI <> 0 excludes new items.
There should be 15 items of which 7 have usage.
15. Close the Major Price Movements database.
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Conclusion
Most items have had modest price increases. Items should be referred to the client to ensure the prices are correct.
Analyzing Profit Margins
1. Ensure Inventory at Dec 31 2015 is selected as the active database and the Data property is selected in the Properties window.
2. Double-click in the Database window to access the Field Manipulation dialog box.
3. Click Add to add the following Virtual field:
l Field Name: PROFIT
l Field Type: Virtual Numeric
l Field Length: Do not enter
l Decimals: 2
l Description: % profit
4. Click in the Parameter cell to load the Equation Editor and enter the following equation to calculate profit as a percentage of price:
@If (CURSELLPRI = 0, 0, (CURSELLPRI - AV_COST) * 100 / CURSELLPRI)
5. Click the Validate and Exit button, returning to the Field Manipulation dialog box.
6. Click OK on the Field Manipulation dialog box to add the PROFIT field to the database.
7. From the Properties windows, click Field Statistics.
8. When prompted, click Yes to create statistics.
Field: PROFIT
STATISTIC VALUE COMMENT
Minimum -28.33 Loss leaders
Maximum 100.00
Average 56.27 Client considers this correct
The average is meaningful here; however, items with negative margins should be extracted. It is often expected that low volume items should have a high margin and high-volume items should have a lower margin.
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9. Return to the Data property of the Inventory at Dec 31 2015 database.
10. On the Analysis tab, in the Extract group, click Direct.
11. Enter File Name: Negative Profit Items.
12. Click the Equation Editor button and enter the equation: PROFIT < 0.
13. Click the Validate and Exit button on the Equation Editor.
14. Click OK to extract the items satisfying the equation.
Notice that the three items identified have no quantity or usage so they have not caused the client any problems, but the client should still be informed.
15. Close the Negative Profit Items database.
Acquiring the Sales Information
To analyze profit margins by the volume of sales we need the sales information. This is not available on the file but taking the annual usage figure and multiplying by the current selling price can make an approximation.
1. Ensure Inventory at Dec 31 2015 is the active database and the Data property is selected in the Properties window.
2. Double-click in the Database window to access the Field Manipulation dialog box. Click Add to add the following Virtual field:
l Field Name: SALES
l Field Type: Virtual Numeric
l Field Length: Do not enter
l Decimals: 0
l Description: Estimate of sales
3. Click in the Parameter cell to load the Equation Editor and enter the formula:
USAGE * CURSELLPRI
4. Click the Validate and Exit button to accept the equation.
5. Click OK then Yes to add the Virtual field.
6. On the Analysis tab, in the Categorize group, click Stratification to analyze the items in turnover bands.
7. In Field to stratify list box, select SALES.
8. In the Fields to total on list box, select SALES.
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9. Enter the following stratification bands:
>= LOWER LIMIT < UPPER LIMIT
0 10,000
10,000 100,000
100,000 1,000,000
10. Select the Create Database check box and enter the File name: Inventory with Turnover Band.
11. Click OK to perform the Stratification.
12. Ensure Inventory with Turnover Band is the active database.
13. On the Analysis tab, in the Categorize group, click Stratification to analyze the items in turnover bands.
14. In the Group by drop-down list, select STRATUM.
15. In the Field to stratify list box, select PROFIT.
16. In the Fields to total on list box, select PROFIT.
17. Enter the following stratification bands:
>= LOWER LIMIT < UPPER LIMIT
(50.00) 0.00
0.00 10.00
10.00 20.00
20.00 30.00
30.00 40.00
40.00 50.00
50.00 75.00
75.00 100.00
18. In the Result name field, enter Profit Stratification.
19. Click OK to perform the Stratification.
20. View the stratification report for the first key (i.e., Stratum 1) in the Profit Stratification output of the Database window.
21. From the STRATUM= drop-down list, select each stratum in turn and view the Stratification Reports to view a separate stratification of profitability for each strata of turnover.
22. Close all databases.
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Exercise 4I: Stratified Random Sampling
When analyzing a group with similar characteristics, you may find that the population size is too large to test individually. Sampling lets you take on a feasible approach by selecting a small group that is representative of the population. The Stratified Random Sampling task lets you draw a random sample from a division or grouping of a population with consideration to the monetary value within each stratification band or stratum. The sample is taken from a specified number of records or percentage of records within each stratum. In essence, this technique balances impartiality with materiality.
Objective:
Extract a random sample based on TOTALCOST using the Stratified Random Sample task.
Exercise Description:
l Select the stratification type
l Select the field to stratify
l Select the field to total
l Specify the increment (for Numeric and Date fields only)
l Specify the stratification bands
l Specify a key (or sequence order)
l Specify the sample size within each stratum
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To perform the test:
1. Ensure Inventory at Dec 31 2015 is the active database.
2. On the Analysis tab, in the Sample group, click Other and then click Stratified Random.
The Stratification Assistant appears. You can select the type of stratification you want to perform.
3. Select the Perform a numeric stratification option and click Continue.
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4. In the Stratification dialog box, select TOTALCOST for both Fields to Stratify and Fields to total on. Enter the values for the lower and upper limit and set the increments according to the guide below:
>=LOWER LIMIT < UPPER LIMIT COMMENTS
(67.000) 0 Increment of 67
0 500.000 Increment of 500
500.000 1,000.000
1,000.000 2,000.000 Increment of 1,000
2,000.000 3,000.000
3,000.000 4,000.000
4,000.000 5,000.000
5,000.000 10,000.000 Increment of 5,000
10,000.000 100,000.000 Increment of 90,000
5. In the File name field, enter Stratified Random Sample and click OK.
The Stratified Random Sample dialog box appears and displays the number of records within each specified stratum.
6. Enter the number of records or the percentage of records under Sample Size within each stratum. In this instance, follow the guide below to complete the sample grouping:
Low Stratum High Stratum Num of Records Sample Size %
-67.00 0 0 0.00
-67.00 0.00 3 0 0.00
0.00 500.00 567 28 4.94
500.00 1000.00 101 5 4.95
1000.00 2000.00 54 3 5.56
2000.00 3000.00 16 0 0.00
3000.00 4000.00 6 0 0.00
4000.00 5000.00 3 0 0.00
5000.00 10000.00 9 9 100.00
10000.00 100000.00 8 8 100.00
100000.00 0 0 0.00
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7. In the Random number seed field, enter 3293.
8. Accept the default File name and click OK.
The Stratified Random Sample task generates a database containing 53 records that were selected based on total cost to test for existence.
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Exercise 4J: Creating Dashboards with Visualize
Objective:
Run the Visualize task to create a dashboard that displays information on the active database in a graphic form.
Exercise Description:
In this exercise, you create a dashboard based on data within the databases in your current project.
l Create a dashboard
l Select the appropriate field statistics to display
l Select the type of charts to showcase, as well as the fields to analyze
Differences Between the Discover and Visualize Tasks
The Visualization feature contains two tasks:
l Discover - automatically generates a dashboard using Analytic Intelligence technology to profile the data in the active database.
l Visualize - lets you build a custom dashboard with user-defined databases and fields. You can compare and display information from different databases on the same dashboard.
Building a Dashboard to Display Inventory Data
1. Ensure Inventory at Dec 31 2015 is the active database and the Data property is selected in the Properties window.
2. On the Analysis tab, in the Visualization group, click Visualize.
After the Visualize task initiates, the Select a Dashboard window appears and lists all saved dashboards associated with the active project.
Exercise
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3. Click New Dashboard to create a new dashboard.
The Chart Properties dialog box appears. You must first select a chart type. The Chart Type drop-down list displays various types of charts that can be presented in the dashboard.
4. From the Chart Type drop-down list, select Column Chart.
Chart-specific properties appear.
5. Click the Browse button adjacent to the Database field. In the Select Database dialog box, select Inventory at Dec 31 2015 and click Select.
You are returned to the Chart Properties dialog box.
6. From the Group by drop-down list, select DEPOT.
7. Ensure Sum is selected as the Statistic.
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8. From the Field to Sum drop-down list, select TOTALCOST.
9. Click Save.
The Select a Layout window appears. You can select the number of chart panels (up to four) to display in your dashboard. You can use the following two methods to scroll through the selections:
l Click the Next or Previous buttons.
l Click one of the layout option buttons .
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10. Click the fourth layout option button to select four chart panels as the layout and click Select.
A dashboard appears. The dashboard consists of two main sections; a row of fields statistic panels at the top and chart panels below. The Sum of TOTALCOST by DEPOT chart appears in the top left chart panel.
11. Click anywhere in the adjacent chart panel to create a second chart.
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12. In the Chart Properties dialog box, set the following parameters:
l Chart Type: Pie Chart
l Database: Major Price Movements
l Group by: DEPOT
l Statistic: Sum
l Field to Sum: PRICE_MOV
13. Click Save.
The Pie chart is added to dashboard.
14. Click anywhere in the first field statistic panel.
The Field Statistic Properties dialog box appears.
15. From the Field drop-down list, select TOTALCOST.
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16. From the Field statistic list, select Net Value.
17. Click Save.
The field statistic is added to the dashboard. You can click in the remaining empty panels to add additional field statistics or charts.
18. From the dashboard title bar, click the Save button .
19. In the Save dialog box, enter Inventory Dashboard as the File name and click Save.
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20. Close the dashboard to return to the IDEA window.
The dashboard is listed in the Library window under the Visualization group. You may need to refresh the Library window for the file to appear in the list.
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4.5 Audit Findings The following findings should be reported to the client:
l About 13% of the product lines have excessive quantities on stock and a provision of about $150,000 is suggested.
l The re-ordering system is not working correctly, and items have been identified for investigation.
l Price increases and profit margins are, overall, reasonable but exceptional items have been identified for follow-up
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Section 5
Common Options
5.1 Overview This section of the Workbook provides instructions for options common to all audits, including:
l Changing the view of the data and designing reports
l Printing (including using the Print Preview task)
l Re-running a task
l Housekeeping
l Reviewing and documenting audits
l Managing the use of IDEA
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5.2 Designing a Report The view of the data may be changed for displaying on the screen and printing.
Options include specifying:
l The fields to view and in what order
l The display format of the fields including font, color, and data type specific parameters
l The width of the fields
l How to stack fields vertically, (i.e., addresses) if required
l Hiding Fields not required
l Freezing columns on the screen when scrolling
l The sequence of records
l Criteria (e.g., AMOUNT > 1000) as a filter on which records are displayed
Views can be changed, saved, and retrieved through the View tab as required. Views can also be applied to other databases of the same format, which is useful for organizational testing on a regular basis.
Further options are defined using the Report Assistant, including:
l Horizontal or vertical print
l Show record numbers on the report
l Display field headings and alignment
l Control breaks
l Sub-totals and totals
l Option to print a cover page for the report
All changes/settings will be stored with the last view and will remain current unless changed by opening a different view or resetting the view settings.
This exercise will show you how to change the view of your database for viewing on the screen and for printing. Instructions are given for the Accounts Payable database. However, the procedure may be applied to any database.
5.2.1 Modifying the View of the Database
1. Open the Accounts Payable project by clicking the Home tab and then from the Projects group, clicking Open.
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The project may be selected from the Recent Projects list where a list of the most recently accessed projects is available.
2. Open the Accounts Payable database and then select to display the fields in the following order:
SUPPNO, PAYEE, PAY_DATE, CHECK, AMOUNT, AUTH
The order of fields may be changed by clicking on the field heading to highlight the entire column, release, then click and drag it to the required position. The field will drop to the right of the red line.
3. To hide fields, from the View tab, select the required fields, and then from the Display group, click Hide Fields.
4. Modify the column settings for each field as outlined below. To select a field to modify, right click over a field header and then select Column Settings.
Alternatively, from the View tab, in the Format group, click the Column Settings button.
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5. Modify the settings for the SUPPNO and PAYEE fields as follows:
l Alignment: Left
l Font: Bold
l Text Color: Blue
l Background Color: Light Grey
Holding down the Ctrl key will allow you to select more than one field in the Column Settings dialog box.
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6. Next, modify the PAY_DATE field as follows:
l Alignment: Left
l Font: Regular
l Date Format: Month Day, Year (i.e., December 3, 1996)
7. Modify the AMOUNT field as follows:
l Alignment: Right
l Use Currency Symbol
l Use Thousands Separator
l Negative Format: (1.00) in red
l Text Color: Red
8. When all column settings are changed, click OK. Adjust each column width to allow for the full text of the field names and data.
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5.2.2 Saving the View
1. On the View tab, in the Views group, click Save.
2. Save the view as Accounts Payable in the Other.ILB Folder. The file will have a VW2/VWM file extension.
3. Click Save to save the settings and return to the Database window.
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5.2.3 Resetting the View
The view settings will remain current until changed, even if the database is closed and re- opened.
To reset the view to the defaults (as when imported), from the View tab, in the Views group, click Reset.
5.2.4 Opening a View
To apply the saved view, on the View tab, in the Views group, click Open. Navigate to (if necessary) and select the required view (Accounts Payable.vw2).
5.2.5 Page Setup
1. On the File tab, click Print and then Page Setup to specify the page setup including orientation and margins.
2. Specify Portrait orientation.
3. Adjust or accept the margins.
4. Click OK.
The current page setup is applied to all database views and reports. The setup is not saved in the view of the database.
5.2.6 Creating a Report
1. On the File tab, click Print and then click Create Report to create a report using the view settings.
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The Report Assistant appears.
2. Accept all defaults and click Next.
IDEA allows you to specify the font, alignment and name for each heading.
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3. Modify the report headings as follows:
FIELD NAME HEADING
SUPPNO SUPPLIER NUMBER
PAYEE PAYEE
PAY_DATE PAYMENT DATE
CHECK CHECK NUMBER
AMOUNT PAYMENT AMOUNT
AUTH AUTHORIZED BY
4. Click Next.
The following step is used for defining if and where control breaks (required for sub- totals in reports) are required.
There can be up to eight fields in an index/ sort order; therefore, up to eight break levels can be defined.
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5. Sequence the database records in the following order:
l SUPPNO: Ascending
l PAY_DATE: Descending
6. Click Next.
The records will be displayed in the order of the index.
The index description will be displayed in the Available Indices box in the center of the Database toolbar.
The following step is only provided if at least one field was selected in the previous Define Breaks step.
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7. Create a break and total the AMOUNT field for each supplier number. Complete the options as in the dialog box below. Click Next.
8. Create grand totals for the AMOUNT field and set the font to bold. Click Next.
Grand totals can be included whether break totals were requested or not. You can alter the appearance of the totals and include currency symbols.
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9. Select the Print cover page option and enter appropriate headings.
l Title: Accounts Payable Report
l Comments: Ordered by Supplier Number and Payment Date
l Prepared by: Enter your initials (or accept the default user name)
l Header: Enter your organization’s name
l Date/Time: Leave defaults unless you have preferences
10. Click Finish.
You will not see any changes on the screen. The options you have selected/changed affect how the view will be printed.
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5.3 Print Preview and Print the Report
5.3.1 Print Preview
1. Once the report has been created using the Report Assistant, you will be prompted to preview the report created. Click Yes.
Alternatively, click No and then on the File tab, click Print Preview.
2. If prompted, reduce the font size to fit to page.
3. View the report, toggling between a single page and two pages.
4. Zoom in to view the report in detail.
5. Check that all settings, including field widths, are correct. Adjust any settings, if necessary.
If you wish to print the view, click the Print button on the Print Preview window.
6. Close the Print Preview window.
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7. On the View tab, in the Views group, click Save to save the view of the database. Select and save the view as Accounts Payable.vw2.
If column widths in the database report are too narrow, the field contents are displayed as follows:
l Character field: Text is truncated from the right
l Numeric field: Contents displayed as ##########
l Date field: Dates are truncated from the right Field widths can be adjusted in the Data property before printing, if necessary.
5.3.2 Printing the Report
Within IDEA, the data and the following reports can be printed and used for testing, reviewing, audit reports, or for audit working paper documentation.
l Database window
l Database view — the data in the current format.
l History view — the History log (for review and documentation).
l Field Statistics view — the statistics for each Numeric and Date field in the database.
l Results views — which holds the reports for the following tests:
l Stratification
l Summarization (optional)
l Aging
l Gap Detection
l Sampling
l Chart Data
l Benford’s Law
l Pivot Table
l Correlation
l Trend Analysis
l Time Series
l Macro window
l IDEAScript - the source code for any scripts.
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IDEA uses standard Windows print routines and will recognize all printers and fonts installed through Microsoft Windows.
Many reports and views may also be printed through the Print Preview option, either through the File tab, Report Assistant, or if a Print Preview button is available on the view's toolbar.
Before selecting the print option, ensure the printer options have been correctly set through Print Setup.
Select the required Database, History, or Results output to be printed then click the Print button on the view's toolbar (if applicable), or on the File tab, click Print.
The Print dialog box will be displayed. Specify the print options required.
It is important when printing a report, to also print the History and attach it to the database report for review.
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5.4 Re-Run a Task The Re-run task is used to re-run the last task (e.g., Extraction, Stratification, Summarization, etc.) with different parameters. This is particularly useful for correcting errors or alternate scenarios.
When running a task in IDEA, the dialog box settings are saved. When Re-run task is selected, the dialog box is populated with the saved settings. A numeric suffix is added to the result or database names. Any or all parameters may then be modified as required to re-run the test.
A task may also be re-run from the IDEAScript code stored in the History log of the database.
To re-run a task, ensure the resultant (child) database created from the task is the active database then re-run the task on any parent database using the following steps:
On the Analysis tab, in the Tasks group, click Re-run.
The task dialog box with the parameters for the last test will be displayed. Modify any parameters as required and then click OK to re-run the task.
The Re-Run task can also be used to re-do an import.
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5.5 Housekeeping IDEA creates databases from most tasks with the consequence that the list of databases builds up quickly. Therefore, housekeeping routines (including backing up data and deleting unwanted files) are important when working with IDEA.
Housekeeping functions such as deleting and renaming databases are carried out from within IDEA. However, functions such as backing up and restoring databases are carried out through the Windows File Explorer.
5.5.1 Managing Project Folders and Project Properties
All the files relating to one project/audit should be kept in a separate project folder. This makes it easier to locate and manage the files and reports relating to an audit and reduces the risk of overwriting files. It is also easier to back up all the files pertaining to each audit once it is complete.
The folder can be created using Microsoft Windows File Explorer or through the File Explorer window in IDEA. Also, IDEA will automatically create a folder when the Managed projects is selected.
It is not necessary to store the original databases/files to be imported in this folder, but it is recommended that the files be stored under the Source Files.ILB folder. Any databases generated by IDEA will be stored in this project folder.
Once the project has been created, it is necessary to enter project information within IDEA. This information will then be printed on all reports.
Creating a New Project Folder and Enter Project Properties
Create the new project, as done in previous exercises of the Workbook.
Create the required project and enter the appropriate project information in the Project Properties dialog box.
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Modifying the Project Properties
To modify the project properties, on the Home tab, in the Projects group, click Properties. Make any necessary changes and click OK.
Opening a Project
To work with another project, on the Home tab, in the Projects group, click Open. If any databases are open in the current folder, you will be asked if you wish to close these before opening the new project.
Once project is set, it remains the active project until changed.
5.5.2 Backing Up/Restoring Data Files
You may wish to backup files for a variety of reasons (i.e., to transfer files to another computer, to backup files according to risk/criticality of data, or because of disk space restrictions).
IDEA uses a single compound file with and IMD file extension to store all the information associated with a file, including the data, the schema, the History log, notes, indices, and test reports.
Each of these files will be stored in its project folder as specified from the File menu. However, the view files, equation files, definition files, and any text files created are not stored within the compound file. Files are stored as follows:
FILE FOLDER NAME
Data and related files Project folder Filename.imd
Views Other.ILB folder Viewname.vw2
Equations Equations.ILB folder Equationname.eqx
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FILE FOLDER NAME
Record definitions Import Definitions.ILB folder Filename.rdf
Print Report templates Results.ILB folder Filename.jpm
Project information Project folder Client.inf
IDEAScript source code Macros.ILB folder Scriptname.iss
Use File Explorer or any other backup facility to back up the required files.
Use compression utilities, such as WinZip or 7-Zip to compress the file before backing it up to reduce the file size.
5.5.3 Using the File Explorer
Databases can be accessed and managed through the File Explorer window. The File Explorer window displays a list of available IDEA databases (<filename>.imd) in the selected project. This is a movable, dockable window and may be resized to display the following information about all IDEA databases:
l File name
l Number of records
l File size
l Date and time of last modification
l Date and time of creation
From the File Explorer toolbar, you can access several tasks that let you manage the databases and change the displayed information.
Button Name Description
File Management
Contains tasks that let you create sub-folders and open, copy, move, rename, append and join databases.
File Display Contains tasks that let you change how your files are displayed in the File Explorer.
Refresh List Updates the database list.
Flag File Adds a flag to the selected database to highlight its importance.
Delete Deletes the selected databases and folders.
You can also right-click on databases in the File Explorer window to access more tasks.
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Creating a Sub-folder in the Project
You can use the Create Folder command in the File Explorer to create a new folder to organize displayed databases.
To create a new sub-folder within the current project folder:
1. From the File Explorer toolbar, click the File Management button and then click Create Folder.
The Create New Folder window appears.
2. Enter a name for the new sub-folder, and then click OK.
3. Drag databases displayed in the File Explorer into the new folder.
IDEA displays the moved databases as sub-directories of the folder in both flat and tree views.
Showing the Databases as a Hierarchy
By default, databases in the File Explorer are displayed in a hierarchy, showing the parent - child relationship between databases. Each parent database node may be expanded to show the child databases or collapsed to the parent level, as required.
If you do not want to view the databases as a hierarchy, from the File Explorer toolbar, click the File Display button and then click Show Hierarchy to clear the selection.
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Changing the Sorted Order
When the Show Hierarchy is turned off, the databases may be sorted by any information parameter in the File Explorer window, such as by file name or file size.
Click the Show Hierarchy button. Double-click the heading bar of the parameter to sort the databases in the ascending order. Double-click the heading bar again to sort the databases in descending order.
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Refreshing the File Explorer
To update the IDEA File Explorer, from the File Explorer toolbar, click the Refresh List button or press the F5 key.
5.5.4 Deleting Data Files
If a database is no longer required, it can be deleted to optimize disk space. You cannot delete an open database; however, you can use the active database.
Deleting the Active Database
1. On the File tab, click Database.
2. On the Database page, click Delete Database.
3. Click Yes to confirm the deletion.
Deleting Selected Databases
1. Ensure all databases are closed.
2. In the File Explorer window, expand all nodes to display all databases.
3. Select the databases to be deleted.
4. From the File Explorer toolbar, click the Delete button.
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5. Click Yes to confirm the deletion.
If you want to use the Recycle Bin when deleting files, ensure the option is selected in the IDEA Options dialog box. You can access the IDEA Options dialog box on the File tab.
If deleting a database that does not reside on your local machine, but on a server, it is moved to the recycle bin.
Deleting Other Files
Use the Delete command in the Library window to delete other files, such as record definitions, views, and equations.
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5.5.5 Copying Data Files
It is sometimes necessary to have the same database in two different project folders. IDEA allows databases to be copied to multiple project folders.
To copy one or more selected databases to another project folder:
1. In the File Explorer window, right-click the required database and click Copy To....
To select multiple database, use the Ctrl or Shift key while selecting.
The Browse For Folder dialog box appears.
2. Navigate to and select the required folder.
3. Click OK.
5.5.6 Moving Data Files
To move one or more selected databases to another project folder:
1. In the File Explorer window, right-click the required database and click Move To....
To select multiple database, use the Ctrl or Shift key while selecting.
The Browse For Folder dialog box appears.
2. Navigate to and select the required folder.
3. Click OK.
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5.5.7 Renaming Databases
Database names may be changed and recorded in the History log.
To rename a database:
1. Make sure the database is closed.
2. Click on the database name in the File Explorer window.
3. Edit the database name as required when the editing option becomes available.
Alternatively, right-click on the selected database name in the File Explorer window and select Rename.
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5.6 Review andHousekeeping Standards should be set for the use of IDEA. These may vary from organization to organization depending on the usage of IDEA and the existing procedures, style, and culture of the organization. However, they should include:
l Objectives
l Required documentation
l Reconciliation/checks on files imported
l Review of work done
l Security
l Backup of work
l Housekeeping
The range of tests available within IDEA is so wide that a considerable amount of time and effort can be wasted on irrelevant tests if objectives are not set. It always pays to know what needs to be achieved.
Documentation depends on an audit department's procedures but should cover all the points listed above, perhaps with standard schedules for reconciliation, file details, and review.
Most files can be matched to known control totals. These may be record counts, totals of certain financial fields, or slightly more complex analysis of certain fields. In addition, it is suggested to match each field in a selection of records with screenshots or printouts to check that they have been imported correctly.
Mistakes can be made in using IDEA as with any technique. Hence, the work should be reviewed. Review procedures are often compliance based, checking that documentation is complete and that reconciliations have been carried out.
IDEA maintains a History log of all operations carried out on a database, including its import and each audit test. It produces a linear log so that the process of how the database was created can be traced. The History log cannot be modified or deleted.
The most common errors are:
l Incorrectly specified formula
l Joins with primary and secondary databases performed incorrectly
l Incorrect join method used (there are five join methods)
l Incorrect fields totaled on summarization
l On complex work involving several steps, performing joins, summarizations, and extractions completed in the wrong order
These can all be verified on a printed (or electronic) copy of the History log.
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The History log is contained within the database's IDEA Merged Document (<filename>.imd).
5.6.1 Viewing the History Log of a Database
1. From the Properties window, click History.
2. Each test or function is displayed in a collapsed node with its title, date and time, and the user name.
3. Expand out a node to view its details by clicking on the plus button.
4. On the History toolbar, click the Expand All Details button to expand out all node.
5. Click the minus button to collapse a single node.
6. On the History toolbar, click the Collapse All button to collapse all nodes.
7. To print the History log, on the File tab, click the Print tab and then click the Print button. The History log is printed as displayed, either collapsed or expanded.
5.6.2 Adding a Comment to the Database
The Comments option is used to add a comment to the active database. It may be used to:
l Annotate the audit steps, explaining the objective
l Explain results
l Add review points
l Attach any documentation that can be cut and pasted into the comment
To add a comment:
1. Click the Add comment link in the Comments area of the Properties window.
2. Enter your comment and then close the Database Comments dialog box.
3. View the comment in the Comments area of the Properties window.
To delete a comment:
1. Right-click the comment link in the Comments area of the Properties window.
2. Select Delete comment.
5.6.3 Accessing Project Overview
The History feature shows the actions taken to receive the database results. Project Overview is a graphical overview of the actions performed within a project folder,
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including the creation, deletion and modification of databases. Project Overview records all database interactions and shows the complete history of what has transpired within the Working Folder. In the graphical format of Project Overview, IDEA allows you to create a Visual Script or IDEA Script macro. A table view format is also available.
To access Project Overview:
On the Home tab, in the Projects group, click Project Overview.
Alternatively, in the File Explorer window, right-click on the database and click Find Database in Project Overview....
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5.7 Managing the Use of IDEA To get the most out of IDEA it needs to be used for an appropriate purpose and its use kept to the objectives. There may be an initial cost in downloading data. The payback depends on the audit objectives and scope.
In managing the use of IDEA, the following issues need to be considered:
l Check that the planning has identified a series of good objectives. These may be analysis work, exception tests, duplicates, matching or any of the tests that IDEA will perform.
l Check that work is not being duplicated. If IDEA is being used, then it is likely that some manual work or tests can be dropped (unless IDEA is being used for new or additional exercises).
l The time to be spent on using IDEA should be budgeted. The time to perform tests and create reports is relatively brief. What can take considerable time is follow-up on items in the reports.
l In addition to audit purposes, IDEA can generate several very useful reports for management. The auditee’s needs should be considered and any extra value that can be obtained added.
Another consideration is compliance with all relevant legislation. Some countries have laws regarding the use of data, notably personal data, such as Canada's Privacy Act. Therefore, care needs to be taken that no legislation is being breached.
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Section 6
Other Uses of IDEA
6.1 Introduction The audit manager of Dynamic Accountants has completed the year-end audit. Both the audit manager and the Chief Financial Officer (CFO) of Bright IDEAs Inc. were satisfied with the work that was completed for the previous sections. The CFO was especially pleased with the computer assisted audit techniques used on the engagement.
The CFO is planning on engaging Dynamic Accountants to assist Bright IDEAs Inc. with implementing IDEA within their organization. However, before the CFO plans to pursue this strategy, he wanted to know what other tests can be completed within the organization.
This section summarizes other uses of IDEA that could be considered.
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6.2 Case Scenario: Other Uses of IDEA The CFO of Bright IDEAs Inc. wanted to know what other tests can be completed within the organization. Some areas that he is specifically interested in are:
l Sales transactions: Bright IDEAs Inc. recently implemented a new billing system that connects to the existing inventory system. The sales managers are concerned about the billing process. Based on their analysis of the financial statements, there seems to be a discrepancy between the increase in inventory and sales revenue.
l Travel expenses: Sales personnel can expense amounts related to travel for all trips made outside their designated areas. Travel expenses have been increasing and the CFO would like an explanation.
l Payroll: Bright IDEAs Inc. has rolled out a plan to offer early retirement to certain employees. The company is interested in costs savings and ensuring that the company retains sufficiently qualified personnel within their departments. The company is also concerned about complaints that they have received from their employees regarding their bonuses. The bonus is calculated based on seniority, number of days absent, and the employee’s annual evaluation score.
l Security: As part of its monitoring of its security practices, management wishes to assess the company’s controls over access to the system and related data.
The company has approached you for advice on tests that could be done using IDEA to address these issues.
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6.3 Other Potential Tests
6.3.1 Sales Transactions
Mechanical Accuracy and Valuation
The following tests could be conducted:
l Total, sub-total by period, category, and division.
l Check sales tax treatment.
l Check discounts offered for adherence to company policy.
l Check commission sales for compliance with company policy.
Analysis
The following analyses could be conducted:
l Summarize sales by period, category, division, and so on.
l Summarize sales returns by period, category, division, and so on.
Existence and Validity
The following tests could be conducted:
l Exception testing: Analyze sales transactions, isolating unusual transactions for review.
l Statistical sampling: Select a statistical sample for compliance testing of attributes such as approvals and supporting documents.
l Duplicates testing: Test for duplicate sales entries.
Completeness
The following tests could be conducted:
l Gap detection: Test the sales files for missing invoice numbers.
l Matching: Reconcile sales invoices, customer orders, and shipping notes.
Identify goods shipped, but not invoiced. This can be achieved by a 3-way match using the common key between the documents (e.g., purchase order number).
Cut-off
The following test could be conducted:
l Check sales cut-off by checking shipment date and/or input date.
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6.3.2 Travel Expenses
Mechanical Accuracy and Valuation
The following test could be conducted:
l Total, sub-total by period, category, and division.
Analysis
The following analysis could be conducted:
l Summarize expense claims by employee and determine if some employees have unusually high totals.
Existence and Validity
The following tests could be conducted:
l Exception testing: Identify receipts submitted exceeding the maximum rate (i.e., as per company policy). Identify sales personnel without any expense claims (they could be fictitious employees).
l Statistical sampling: Select sample of expense claims and ensure that they comply with company policies and are approved by the appropriate personnel.
l Duplicates testing: Identify duplicate expense claims.
Cut-off
The following test could be conducted:
l Check if there are expenses claimed by the employee after the date of termination.
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6.3.3 Payroll
Mechanical Accuracy and Valuation
The following tests could be conducted:
l Re-perform calculation of net pay.
l Recalculate the allocation of shares to a company pension plan based on the prescribed formula.
l Recalculate investment income allocated to each employee.
Analysis
The following analyses could be conducted:
l Summarize payroll expenses (e.g., by distribution category).
l Perform month-to-month or year-to-year comparisons (e.g., number of employees, hours worked, and gross pay).
l Determine the impact of the early retirement program within given areas. For example, areas with a significant number of early retirees could face a severe shortage of resources to achieve specified goals.
l Identify hiring needs based on departures.
Existence and Validity
The following tests could be conducted:
l Exception testing:
l Analyze the stream of payments and extract unusual items (e.g., excessive hours and rates).
l Identify blank or invalid Social Insurance Numbers.
l Identify employees without standard information (e.g., location, email, and extension).
l Identify employees without pay, negative pay, or without deductions.
l Statistical sampling: Select a statistical sample of payments for compliance and/or substantive verification against supporting documentation in personnel files.
l Duplicates testing: Identify duplicate Social Insurance Numbers.
Completeness
The following tests could be conducted:
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l Gap detection: Identify gaps within sequence of employee numbers and obtain explanations for those gaps.
l Matching: Match gaps in employee numbers against list of terminated employees.
Cut-off
The following test could be conducted:
l Check for payments to employees after the date of termination and follow up to ensure the payments were justified.
6.3.4 Access Controls
The following analyses could be conducted to test the effectiveness of access controls:
Analysis
l Summarize access privileges by employee and determine whether employees have incompatible access privileges (to ensure that segregation of incompatible functions is enforced).
l Summarize employee access privileges and determine whether employees have greater access privileges than they need to perform their jobs (to ensure that the principle of least privilege is enforced).
l Summarize system accesses outside of normal business hours by employee and determine whether there is unusual activity that needs to be followed up.
Exception Testing
l Identify user accounts that deviate from the prescribed password policy.
l Identify user accounts that do not correspond to the normal naming conventions.
l Identify inactive or dormant accounts (i.e., access codes that have not been used within the past 3-6 months. Such accounts should be purged from the system to prevent their abuse).
Statistical Sampling
l Select sample of employees and check that they were properly screened prior to being given access to sensitive information.
l Select sample of accesses and trace to current employee file. Match user profiles and privileges documented in the employee file to the user profile maintained in the operating system (to ensure that only authorized personnel have access to the system and that the privileges in effect do not exceed those granted by the business unit).
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Duplicates Testing
l Identify employees with multiple access codes and determine whether all codes are needed. (Extra/infrequently used codes represent a security risk).
Matching
l Reconcile vendors listed in master file to independent authorized list of vendors (to ensure that systems personnel have not bypassed normal master file authorization procedures).
l Summarize all users accessing the system and match users accessing the system to employee file.
Cut-off
l Check list of terminated employees against users accessing the system after the date of termination.
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CaseWare IDEA Inc.
1400 St. Laurent Blvd., Suite 500 Ottawa, ON K1K 4H4
Canada 1-800-265-4332
idea.caseware.com
- Section 1
- Introduction to the IDEA Workbook
- 1.1 Setting the Scene
- 1.2 How this Workbook is Organized
- 1.3 Overview of the Use of IDEA
- 1.3.1 Stages of Using IDEA
- 1.3.2 Determine How IDEA is Best Utilized
- 1.4 Audit Objectives
- 1.4.1 Mechanical Accuracy and Valuation
- 1.4.2 Analysis
- 1.4.3 Existence and Validity
- 1.4.4 Completeness
- 1.4.5 Cut-off
- 1.4.6 Other Audit Objectives
- 1.5 Determining Data Requirements
- 1.5.1 Areas to Consider
- Section 2
- Accounts Receivable Audit
- 2.1 Introduction
- 2.1.1 Potential Risks
- 2.1.2 Potential Tests
- 2.2 Case Scenario: Accounts Receivable
- 2.3 Obtaining the Data
- 2.3.1 Requesting Data Files for Audit Purposes
- 2.4 Audit Program
- 2.4.1 Audit Objective
- Exercise 2A: Audit Setup
- Exercise 2B: Importing the Accounts Receivable Transactions File
- Exercise 2C: Selecting a Control Total Field
- Exercise 2D: Generating Field Statistics
- Exercise 2E: Reconciling the Database
- Exercise 2F: Random Record Sampling
- Exercise 2G: Age Analysis
- 2.5 Understanding the Database Window
- 2.5.1 Data
- 2.5.2 History
- 2.5.3 Field Statistics
- 2.5.4 Results
- Exercise 2H: Extracting High Value and Old Items
- Exercise 2I: Identifying and Reviewing All Credit Notes
- Exercise 2J: Calculating the Net Transaction Amount
- Exercise 2K: Analyzing the Balances and Taxes by Account
- Exercise 2L: Checking Debtors Against Authorized Credit Risk
- 2.6 Audit Findings
- Section 3
- Accounts Payable Audit and Fraud Investigation
- 3.1 Introduction
- 3.1.1 Potential Risks
- 3.1.2 Potential Tests
- 3.2 Case Scenario: Accounts Payable
- 3.3 Obtaining the Data
- 3.3.1 Requesting Data Files for Audit Purposes
- 3.4 Audit Program
- 3.4.1 Audit Objective
- Exercise 3A: Audit Setup
- Exercise 3B: Importing the Accounts Payable and Authorized Suppliers Data Files
- Exercise 3C: Verifying that the Data Files Have Been Correctly Imported
- Exercise 3D: Identifying Trends, Patterns, Duplicates, and Outliers with Discover
- Exercise 3E: Analyzing the Profile of Payments
- Exercise 3F: Identifying High and Unusual Payments
- Exercise 3G: Identifying Exceptional Transactions
- 3.5 Applying Benford’s Law to Identify Exceptional Items
- 3.6 Data-based Conditions for Benford’s Law
- 3.6.1 Geometrical Series
- 3.6.2 Description of the Same Object
- 3.6.3 Unlimited Data Space (Non-Existence of Minima and Maxima)
- 3.6.4 No Systematic Data Structure
- Exercise 3H: Test for Duplicate Payments and Records
- Exercise 3I: Searching for Gaps in the Check Number Sequence
- Exercise 3J: Searching for Gaps in the Check Date Sequence
- Exercise 3K: Analyzing Payment Days to Identify Favorable Terms to Suppliers
- Exercise 3L: Payments to Unauthorized Suppliers
- Exercise 3M: Analyzing Payments by Supplier
- 3.7 Audit Findings
- Section 4
- Inventory Analysis
- 4.1 Introduction
- 4.1.1 Potential Risks
- 4.1.2 Potential Tests
- 4.2 Case Scenario: Inventory
- 4.3 Obtaining the Data
- 4.3.1 Explanation of the ASCII Delimited File Format
- 4.3.2 Requesting Data Files for Analysis Purposes
- 4.4 Work Plan
- Exercise 4A: Project Setup
- Exercise 4B: Importing the Inventory File
- Exercise 4C: Verifying that the Database Has Been Correctly Imported
- Exercise 4D: Identifying Obsolete Inventory Items
- Exercise 4E: Calculating Usage Ratios and Suggest Obsolescence Provision
- Exercise 4F: Calculating the Total Provisions for Inventory by Depot
- Exercise 4G: Test the Accuracy of the Automatic Reordering System
- Exercise 4H: Analyzing Selling Prices and Margins
- Exercise 4I: Stratified Random Sampling
- Exercise 4J: Creating Dashboards with Visualize
- 4.5 Audit Findings
- Section 5
- Common Options
- 5.1 Overview
- 5.2 Designing a Report
- 5.2.1 Modifying the View of the Database
- 5.2.2 Saving the View
- 5.2.3 Resetting the View
- 5.2.4 Opening a View
- 5.2.5 Page Setup
- 5.2.6 Creating a Report
- 5.3 Print Preview and Print the Report
- 5.3.1 Print Preview
- 5.3.2 Printing the Report
- 5.4 Re-Run a Task
- 5.5 Housekeeping
- 5.5.1 Managing Project Folders and Project Properties
- 5.5.2 Backing Up/Restoring Data Files
- 5.5.3 Using the File Explorer
- 5.5.4 Deleting Data Files
- 5.5.5 Copying Data Files
- 5.5.6 Moving Data Files
- 5.5.7 Renaming Databases
- 5.6 Review and Housekeeping
- 5.6.1 Viewing the History Log of a Database
- 5.6.2 Adding a Comment to the Database
- 5.6.3 Accessing Project Overview
- 5.7 Managing the Use of IDEA
- Section 6
- Other Uses of IDEA
- 6.1 Introduction
- 6.2 Case Scenario: Other Uses of IDEA
- 6.3 Other Potential Tests
- 6.3.1 Sales Transactions
- 6.3.2 Travel Expenses
- 6.3.3 Payroll
- 6.3.4 Access Controls
IDEA Data Analysis Workbook/Source Files.ILB/Acc_rec2015.accdb
| ACCOUNT_NO | INVOICE_NO | TYPE | GROSS_AMT | GST | PST | PAID_FLAG | DATE | CUST_REF | COMMENT |
|---|---|---|---|---|---|---|---|---|---|
| C020 | 46000 | I | 2345.54 | 153.45 | 173.74 | 11/4/14 | A 5574 | ||
| R008 | 46001 | I | 692.72 | 45.32 | 51.31 | P | 1/4/15 | O 9844 | |
| W008 | 46002 | I | 337.61 | 22.09 | 25.01 | P | 1/4/15 | Q 7639 | |
| D014 | 46003 | I | 2306.54 | 150.9 | 170.85 | P | 1/4/15 | Z 5721 | |
| H014 | 46004 | I | 2306.54 | 150.9 | 170.85 | P | 1/4/15 | W 8122 | |
| M014 | 46005 | I | 1124.15 | 73.54 | 83.27 | P | 1/4/15 | X 2768 | |
| C020 | 46006 | I | 3039.84 | 198.87 | 225.17 | 12/5/14 | X 9574 | ||
| R005 | 46007 | I | 1971.26 | 128.96 | 146.02 | P | 1/5/15 | T 9789 | |
| G020 | 46008 | I | 2345.54 | 153.45 | 173.74 | P | 1/5/15 | S 8350 | |
| M020 | 46009 | I | 3617.01 | 236.63 | 267.93 | P | 1/5/15 | Z 5068 | |
| M123 | 46010 | I | 79.91 | 5.23 | 5.92 | 12/22/14 | V 587 | ||
| T004 | 46010 | I | 39.94 | 2.61 | 2.96 | 1/22/15 | Q 8882 | ||
| F123 | 46012 | I | 50.0 | 3.27 | 3.7 | P | 1/22/15 | K 3449 | |
| T003 | 46013 | I | 3543.54 | 231.82 | 262.48 | 1/22/15 | D 8050 | ||
| N001 | 46014 | I | 3766.58 | 246.41 | 279.01 | 1/22/15 | K 3049 | ||
| N002 | 46015 | I | 24.99 | 1.63 | 1.85 | P | 1/22/15 | C 999 | |
| F123 | 46016 | I | 2100.0 | 137.38 | 155.56 | 1/21/15 | Q 451 | ||
| A123 | 46017 | I | 50.0 | 3.27 | 3.7 | 1/22/15 | M 7134 | ||
| A123 | 46018 | I | 2100.0 | 137.38 | 155.56 | 1/22/15 | S 7316 | ||
| M123 | 46019 | I | 1975.65 | 129.25 | 146.34 | 1/22/15 | W 305 | ||
| G010 | 46020 | I | 2345.54 | 153.45 | 173.74 | 1/23/15 | L 751 | ||
| T010 | 46021 | I | 3689.05 | 241.34 | 273.26 | P | 1/23/15 | G 752 | |
| P010 | 46022 | I | 4133.88 | 270.44 | 306.21 | 1/23/15 | G 7565 | ||
| M010 | 46023 | I | 2093.15 | 136.94 | 155.05 | P | 1/23/15 | F 7554 | |
| B010 | 46024 | I | 2345.54 | 153.45 | 173.74 | P | 1/23/15 | V 5414 | |
| F123 | 46025 | I | 123.76 | 8.1 | 9.17 | 1/29/15 | C 9610 | ||
| N005 | 46026 | I | 238.37 | 15.59 | 17.66 | 1/29/15 | Z 5004 | ||
| M123 | 46027 | I | 218.62 | 14.3 | 16.19 | P | 1/29/15 | V 6578 | |
| T005 | 46028 | I | 421.09 | 27.55 | 31.19 | 1/29/15 | L 6746 | ||
| A123 | 46029 | I | 123.76 | 8.1 | 9.17 | 1/29/15 | G 7264 | ||
| F128 | 46030 | I | 2564.34 | 167.76 | 189.95 | 1/31/15 | N 4301 | ||
| P008 | 46031 | I | 283.98 | 18.58 | 21.04 | 1/31/15 | A 4754 | ||
| P008 | 46032 | I | 4848.66 | 317.2 | 359.16 | 1/31/15 | I 7053 | ||
| F128 | 46033 | I | 2361.0 | 154.46 | 174.89 | 1/31/15 | G 8569 | ||
| A128 | 46034 | I | 2361.0 | 154.46 | 174.89 | 1/31/15 | I 4686 | ||
| A128 | 46035 | I | 2564.34 | 167.76 | 189.95 | 1/31/15 | C 2311 | ||
| M128 | 46036 | I | 1311.12 | 85.77 | 97.12 | P | 1/31/15 | T 4961 | |
| M128 | 46037 | I | 4512.18 | 295.19 | 334.24 | 1/31/15 | J 625 | ||
| T008 | 46038 | I | 9266.43 | 606.22 | 686.4 | 1/31/15 | G 9699 | ||
| T008 | 46039 | I | 145.2 | 9.5 | 10.76 | P | 1/31/15 | Y 1406 | |
| R020 | 46040 | I | 218.16 | 14.27 | 16.16 | 2/4/15 | S 6291 | ||
| H025 | 46041 | I | 1383.92 | 90.54 | 102.51 | P | 2/4/15 | F 7148 | |
| M025 | 46042 | I | 156.51 | 10.24 | 11.59 | P | 2/4/15 | Z 3527 | |
| W020 | 46043 | I | 24.67 | 1.61 | 1.83 | P | 2/4/15 | H 4936 | |
| D025 | 46044 | I | 1383.92 | 90.54 | 102.51 | 2/4/15 | D 5309 | ||
| N005 | 46045 | C | -18.76 | -1.23 | -1.39 | 2/5/15 | N 2077 | WRONG PRICE | |
| M123 | 46046 | C | -64.38 | -4.21 | -4.77 | P | 2/5/15 | I 641 | WRONG PRICE |
| M010 | 46047 | I | 1180.56 | 77.23 | 87.45 | 2/5/15 | V 1548 | ||
| T010 | 46048 | I | 1590.44 | 104.05 | 117.81 | P | 2/5/15 | I 7700 | |
| A123 | 46049 | C | -78.96 | -5.17 | -5.85 | 2/5/15 | U 6881 | WRONG PRICE | |
| P010 | 46050 | I | 1026.61 | 67.16 | 76.05 | P | 2/5/15 | X 9387 | |
| G010 | 46051 | I | 762.04 | 49.85 | 56.45 | 2/5/15 | D 7733 | ||
| F123 | 46052 | C | -78.96 | -5.17 | -5.85 | 2/5/15 | T 9466 | WRONG PRICE | |
| B010 | 46053 | I | 762.04 | 49.85 | 56.45 | 2/5/15 | E 1972 | ||
| T005 | 46054 | C | -15.3 | -1.0 | -1.13 | 2/5/15 | A 5636 | WRONG PRICE | |
| W020 | 46055 | I | 2.34 | 0.15 | 0.17 | 2/7/15 | P 5595 | ||
| C020 | 46056 | I | 762.04 | 9.8 | 5.53 | P | 2/7/15 | W 3121 | |
| D014 | 46057 | I | 862.04 | 56.4 | 63.85 | P | 2/7/15 | J 4936 | |
| G020 | 46058 | I | 762.04 | 49.85 | 56.45 | 2/7/15 | I 5096 | ||
| R005 | 46059 | I | 72.15 | 4.72 | 5.34 | P | 2/7/15 | Q 8508 | |
| M020 | 46060 | I | 578.99 | 37.88 | 42.89 | P | 2/7/15 | J 6738 | |
| R008 | 46061 | I | 816.27 | 53.4 | 60.46 | 2/7/15 | E 9646 | ||
| H025 | 46062 | I | 517.22 | 33.84 | 38.31 | 2/7/15 | B 8291 | ||
| D025 | 46063 | I | 517.22 | 33.84 | 38.31 | 2/7/15 | G 9311 | ||
| W008 | 46064 | I | 752.52 | 49.23 | 55.74 | P | 2/7/15 | F 5011 | |
| M025 | 46065 | I | 70.78 | 4.63 | 5.24 | 2/7/15 | O 384 | ||
| R020 | 46066 | I | 17.12 | 1.12 | 1.27 | 2/7/15 | J 9640 | ||
| K001 | 46067 | I | 568.94 | 37.22 | 42.14 | P | 2/7/15 | T 4893 | |
| M014 | 46068 | I | 794.71 | 51.99 | 58.87 | P | 2/7/15 | A 276 | |
| W005 | 46069 | I | 54.82 | 3.59 | 4.06 | P | 2/7/15 | R 5049 | |
| H014 | 46070 | I | 862.04 | 56.4 | 63.85 | P | 2/7/15 | Y 6063 | |
| H025 | 46071 | I | 1811.1 | 118.48 | 134.16 | 2/12/15 | B 2989 | ||
| M025 | 46072 | I | 2003.34 | 131.06 | 148.4 | 2/12/15 | F 304 | ||
| H025 | 46073 | P | -2000.59 | 0.0 | 0.0 | 2/12/15 | J 2564 | ||
| H025 | 46073 | I | 47.8 | 3.13 | 3.54 | 2/12/15 | J 2564 | ||
| G010 | 46074 | I | 2018.5 | 132.05 | 149.52 | 2/12/15 | R 1827 | ||
| M025 | 46075 | P | -536.05 | 0.0 | 0.0 | 2/12/15 | V 6626 | ||
| M025 | 46075 | I | 255.6 | 16.72 | 18.93 | 2/12/15 | V 6626 | ||
| M010 | 46076 | I | 3334.34 | 218.13 | 246.99 | 2/12/15 | M 1443 | ||
| B010 | 46077 | I | 2018.5 | 132.05 | 149.52 | 2/12/15 | K 8814 | ||
| R025 | 46078 | I | 455.78 | 29.82 | 33.76 | 2/12/15 | Y 3679 | ||
| W025 | 46079 | P | -62.99 | 0.0 | 0.0 | 2/12/15 | R 3885 | ||
| W025 | 46079 | I | 44.66 | 2.92 | 3.31 | 2/12/15 | R 3885 | ||
| W025 | 46080 | I | 504.16 | 32.98 | 37.35 | P | 2/12/15 | K 8307 | |
| T010 | 46081 | I | 3851.6 | 251.97 | 285.3 | 2/12/15 | K 2826 | ||
| P010 | 46082 | I | 2331.63 | 152.54 | 172.71 | 2/12/15 | O 2089 | ||
| R025 | 46083 | P | -235.09 | 0.0 | 0.0 | 2/12/15 | Z 8449 | ||
| R025 | 46083 | I | 13.85 | 0.91 | 1.03 | 2/12/15 | Z 8448 | ||
| M014 | 46084 | I | 3735.46 | 244.38 | 276.7 | 2/16/15 | P 6650 | ||
| D025 | 46085 | I | 1811.1 | 118.48 | 134.16 | 2/16/15 | D 4340 | ||
| W011 | 46086 | I | 1810.29 | 118.43 | 134.1 | 2/16/15 | B 7448 | ||
| R011 | 46087 | I | 1462.84 | 95.7 | 108.36 | 2/16/15 | Q 9494 | ||
| H014 | 46088 | I | 3018.5 | 197.47 | 223.59 | P | 2/16/15 | F 4946 | |
| D014 | 46089 | I | 3018.5 | 197.47 | 223.59 | P | 2/16/15 | Q 4990 | |
| C020 | 46090 | I | 9018.5 | 590.0 | 668.04 | P | 2/16/15 | I 2759 | |
| K001 | 46091 | I | 1992.21 | 130.33 | 147.57 | 2/16/15 | K 6423 | ||
| M020 | 46092 | I | 8780.52 | 574.43 | 650.41 | P | 2/17/15 | B 6356 | |
| W005 | 46093 | I | 13479.34 | 881.83 | 998.47 | 2/17/15 | C 1748 | ||
| G020 | 46094 | I | 9018.5 | 590.0 | 668.04 | 2/17/15 | P 4089 | ||
| R005 | 46095 | I | 13844.67 | 905.73 | 1025.53 | 2/17/15 | D 2370 | ||
| T010 | 46096 | I | 96.52 | 6.31 | 7.15 | 2/18/15 | B 1108 | ||
| B010 | 46097 | I | 289.32 | 18.93 | 21.43 | 2/18/15 | H 1025 | ||
| C020 | 46098 | I | 289.32 | 18.93 | 21.43 | 2/18/15 | F 3582 | ||
| W011 | 46099 | I | 176.3 | 11.53 | 13.06 | P | 2/18/15 | M 3204 | |
| M020 | 46100 | I | 492.29 | 32.21 | 36.47 | P | 2/18/15 | L 3359 | |
| W005 | 46101 | I | 916.0 | 59.93 | 67.85 | 2/18/15 | W 9860 | ||
| M014 | 46102 | I | 268.65 | 17.58 | 19.9 | 2/18/15 | R 1273 | ||
| M010 | 46103 | I | 140.74 | 9.21 | 10.43 | 2/18/15 | J 8216 | ||
| P010 | 46104 | I | 198.43 | 12.98 | 14.7 | 2/18/15 | H 1539 | ||
| G010 | 46105 | I | 289.32 | 18.93 | 21.43 | 2/18/15 | U 4316 | ||
| R005 | 46106 | I | 538.33 | 35.22 | 39.88 | 2/18/15 | J 9259 | ||
| D014 | 46107 | I | 389.32 | 25.47 | 28.84 | 2/18/15 | T 8268 | ||
| G020 | 46108 | I | 289.32 | 18.93 | 21.43 | P | 2/18/15 | P 3317 | |
| D025 | 46109 | P | -2000.59 | 0.0 | 0.0 | 2/18/15 | T 8025 | ||
| D025 | 46109 | I | 20.0 | 1.31 | 1.48 | 2/18/15 | T 8025 | ||
| R011 | 46110 | I | 255.49 | 16.71 | 18.93 | 2/18/15 | J 8049 | ||
| H014 | 46111 | I | 389.32 | 25.47 | 28.84 | 2/18/15 | X 5765 | ||
| A128 | 46112 | I | 320.5 | 20.97 | 23.74 | 2/25/15 | P 5806 | ||
| T006 | 46113 | I | 2706.64 | 177.07 | 200.49 | 2/25/15 | L 798 | ||
| F128 | 46114 | I | 320.5 | 20.97 | 23.74 | 2/25/15 | I 2410 | ||
| F123 | 46115 | I | 1765.25 | 115.48 | 130.76 | 2/25/15 | Q 2700 | ||
| N005 | 46116 | I | 1412.08 | 92.38 | 104.6 | 2/25/15 | M 121 | ||
| T008 | 46117 | I | 81.91 | 5.36 | 6.07 | 2/25/15 | G 716 | ||
| M123 | 46118 | I | 3383.59 | 221.36 | 250.64 | 2/25/15 | G 5145 | ||
| M128 | 46119 | I | 285.74 | 18.69 | 21.17 | P | 2/25/15 | A 6669 | |
| A123 | 46120 | I | 1765.25 | 115.48 | 130.76 | 2/25/15 | J 1347 | ||
| P008 | 46121 | I | 91.87 | 6.01 | 6.81 | 2/25/15 | E 7040 | ||
| B010 | 46122 | I | 1028.05 | 67.26 | 76.15 | 2/27/15 | R 3791 | ||
| M010 | 46123 | I | 827.44 | 54.13 | 61.29 | 2/27/15 | S 9616 | ||
| R010 | 46124 | I | 193.76 | 12.68 | 14.35 | P | 2/27/15 | X 3814 | |
| T010 | 46125 | I | 155.95 | 10.2 | 11.55 | 2/27/15 | A 4633 | ||
| G010 | 46126 | I | 1028.05 | 67.26 | 76.15 | 2/27/15 | H 274 | ||
| Z001 | 46127 | I | 1563.61 | 102.29 | 115.82 | 3/1/15 | D 8963 | ||
| M025 | 46128 | I | 3609.43 | 236.13 | 267.37 | 3/3/15 | F 9804 | ||
| T006 | 46129 | I | 1447.32 | 94.68 | 107.21 | 3/3/15 | I 9795 | ||
| M123 | 46130 | I | 5446.64 | 356.32 | 403.45 | 3/3/15 | Q 4386 | ||
| H025 | 46131 | I | 2416.83 | 158.11 | 179.02 | 3/3/15 | N 1465 | ||
| P006 | 46132 | I | 906.14 | 59.28 | 67.12 | 3/3/15 | N 3432 | ||
| A123 | 46133 | I | 3410.05 | 223.09 | 252.6 | 3/3/15 | A 6056 | ||
| F123 | 46134 | I | 3410.05 | 223.09 | 252.6 | P | 3/3/15 | D 9712 | |
| W007 | 46135 | I | 342.06 | 22.38 | 25.34 | 3/4/15 | W 1685 | ||
| R007 | 46136 | I | 492.72 | 32.23 | 36.5 | 3/4/15 | V 2475 | ||
| C020 | 46137 | I | 1028.05 | 67.26 | 76.15 | P | 3/4/15 | U 9930 | |
| M020 | 46138 | I | 713.71 | 46.69 | 52.87 | 3/4/15 | F 491 | ||
| G020 | 46139 | I | 1028.05 | 67.26 | 76.15 | 3/4/15 | C 2446 | ||
| D014 | 46140 | I | 4028.05 | 263.52 | 298.37 | 3/4/15 | Z 3246 | ||
| R020 | 46141 | I | 901.22 | 58.96 | 66.76 | P | 3/4/15 | H 9480 | |
| M014 | 46142 | I | 1026.45 | 67.15 | 76.03 | 3/4/15 | D 9044 | ||
| H014 | 46143 | I | 4028.05 | 263.52 | 298.37 | 3/4/15 | C 2657 | ||
| W020 | 46144 | I | 229.65 | 15.02 | 17.01 | 3/4/15 | S 8587 | ||
| R025 | 46145 | I | 2961.86 | 193.77 | 219.4 | 3/5/15 | E 3320 | ||
| P009 | 46146 | I | 146.41 | 9.58 | 10.85 | 3/5/15 | L 1592 | ||
| M010 | 46147 | I | 6.15 | 0.4 | 0.46 | 3/5/15 | Q 1708 | ||
| D014 | 46148 | I | 234.51 | 15.34 | 17.37 | P | 3/5/15 | L 1382 | |
| T009 | 46149 | I | 211.26 | 13.82 | 15.65 | 3/5/15 | P 5391 | ||
| C020 | 46150 | I | 234.51 | 15.34 | 17.37 | 3/5/15 | M 5554 | ||
| M128 | 46151 | I | 768.28 | 50.26 | 56.91 | 3/5/15 | V 6983 | ||
| G020 | 46152 | I | 234.51 | 15.34 | 17.37 | 3/5/15 | E 6208 | ||
| H014 | 46153 | I | 234.51 | 15.34 | 17.37 | 3/5/15 | H 3190 | ||
| T010 | 46154 | I | 4.75 | 0.31 | 0.35 | 3/5/15 | I 657 | ||
| W025 | 46155 | I | 4423.41 | 289.38 | 327.66 | 3/5/15 | I 8272 | ||
| B010 | 46156 | I | 10.11 | 0.66 | 0.75 | 3/5/15 | F 9213 | ||
| M020 | 46157 | I | 182.69 | 11.95 | 13.53 | 3/5/15 | G 5542 | ||
| F128 | 46158 | I | 532.45 | 34.83 | 39.44 | 3/5/15 | D 8601 | ||
| W020 | 46159 | I | 1408.55 | 92.15 | 104.34 | 3/5/15 | Y 6368 | ||
| A128 | 46160 | I | 532.45 | 34.83 | 39.44 | 3/5/15 | U 1449 | ||
| R007 | 46161 | I | 53.94 | 3.53 | 4.0 | 3/5/15 | J 6864 | ||
| R020 | 46162 | I | 760.1 | 49.73 | 56.3 | 3/5/15 | Q 8983 | ||
| M014 | 46163 | I | 434.57 | 28.43 | 32.19 | 3/5/15 | W 8318 | ||
| G010 | 46164 | I | 10.11 | 0.66 | 0.75 | 3/5/15 | O 9423 | ||
| R010 | 46165 | I | 7.81 | 0.51 | 0.58 | P | 3/5/15 | R 7450 | |
| W007 | 46166 | I | 42.02 | 2.75 | 3.11 | 3/5/15 | O 4228 | ||
| D025 | 46167 | I | 2416.83 | 158.11 | 179.02 | 3/7/15 | J 1545 | ||
| K001 | 46168 | I | 2658.51 | 173.92 | 196.93 | 3/7/15 | A 5426 | ||
| D025 | 46169 | I | 140.71 | 9.21 | 10.42 | 3/7/15 | I 3289 | ||
| K001 | 46170 | I | 154.78 | 10.13 | 11.47 | 3/7/15 | W 6115 | ||
| M025 | 46171 | I | 95.95 | 6.28 | 7.11 | 3/7/15 | V 7278 | ||
| H025 | 46172 | I | 140.71 | 9.21 | 10.42 | 3/7/15 | U 7856 | ||
| W025 | 46173 | I | 37.55 | 2.46 | 2.78 | 3/9/15 | O 3020 | ||
| R025 | 46174 | I | 55.06 | 3.6 | 4.08 | 3/9/15 | A 5775 | ||
| K001 | 46210 | I | 6682.5 | 437.17 | 495.0 | 3/11/15 | H 6628 | ||
| F128 | 46175 | I | 431.8 | 28.25 | 31.99 | 3/15/15 | U 6317 | ||
| M128 | 46176 | I | 157.08 | 10.28 | 11.64 | 3/15/15 | H 8228 | ||
| P010 | 46177 | I | 286.14 | 18.72 | 21.2 | 3/15/15 | J 6696 | ||
| T010 | 46178 | I | 104.09 | 6.81 | 7.71 | 3/15/15 | B 2884 | ||
| A128 | 46179 | I | 431.8 | 28.25 | 31.99 | 3/15/15 | E 8346 | ||
| F123 | 46180 | I | 1520.98 | 99.5 | 112.67 | 3/17/15 | K 4450 | ||
| P006 | 46181 | I | 2002.03 | 130.97 | 148.3 | 3/17/15 | O 7000 | ||
| T006 | 46182 | I | 3311.46 | 216.64 | 245.29 | 3/17/15 | Z 8470 | ||
| A123 | 46183 | I | 1520.98 | 99.5 | 112.67 | 3/17/15 | V 9359 | ||
| M123 | 46184 | I | 2515.78 | 164.58 | 186.35 | 3/17/15 | A 4531 | ||
| T010 | 46185 | C | -1588.16 | -103.9 | -117.64 | 3/18/15 | I 9884 | ||
| A128 | 46186 | C | -431.0 | -28.2 | -31.93 | 3/18/15 | O 624 | ||
| B008 | 46187 | C | -431.0 | -28.2 | -31.93 | 3/18/15 | X 857 | ||
| F128 | 46188 | C | -431.0 | -28.2 | -31.93 | 3/18/15 | Z 1538 | ||
| F130 | 46189 | C | -431.0 | -28.2 | -31.93 | 3/18/15 | C 605 | ||
| T010 | 46190 | C | -1.54 | -0.1 | -0.11 | 3/18/15 | O 6826 | ||
| M128 | 46191 | C | -634.79 | -41.53 | -47.02 | 3/18/15 | W 5138 | ||
| M130 | 46192 | C | -44.45 | -2.91 | -3.29 | 3/18/15 | F 6273 | ||
| P010 | 46193 | C | -1078.31 | -70.54 | -79.87 | 3/18/15 | S 5252 | ||
| P010 | 46194 | C | -14.93 | -0.98 | -1.11 | 3/18/15 | Q 6421 | ||
| R010 | 46195 | I | 0.33 | 0.02 | 0.02 | 3/21/15 | Q 1563 | ||
| W025 | 46196 | I | 16095.58 | 1052.98 | 1192.27 | 3/21/15 | V 7610 | ||
| W020 | 46197 | I | 17307.59 | 1132.27 | 1282.04 | 3/21/15 | P 4077 | ||
| R020 | 46198 | I | 10564.84 | 691.16 | 782.58 | 3/21/15 | J 1497 | ||
| R025 | 46199 | I | 13001.28 | 850.55 | 963.06 | 3/21/15 | Q 3676 | ||
| D025 | 46200 | I | 6075.0 | 397.43 | 450.0 | 3/21/15 | Y 5531 | ||
| C020 | 46201 | I | 34.0 | 2.22 | 2.52 | 3/21/15 | R 3216 | ||
| W007 | 46202 | I | 58.34 | 3.82 | 4.32 | 3/21/15 | T 3403 | ||
| B010 | 46203 | I | 10.34 | 0.68 | 0.77 | 3/21/15 | E 9337 | ||
| D014 | 46204 | I | 10125.0 | 662.38 | 750.0 | 3/21/15 | P 6210 | ||
| T010 | 46205 | I | 0.01 | 0.0 | 0.0 | 3/21/15 | W 27 | ||
| M014 | 46206 | I | 16587.03 | 1085.13 | 1228.67 | 3/21/15 | B 2960 | ||
| M020 | 46207 | I | 46.83 | 3.06 | 3.47 | 3/21/15 | D 3128 | ||
| G010 | 46208 | I | 10.34 | 0.68 | 0.77 | 3/21/15 | Y 7937 | ||
| G020 | 46209 | I | 34.0 | 2.22 | 2.52 | 3/21/15 | A 8975 | ||
| H014 | 46211 | I | 10125.0 | 662.38 | 750.0 | 3/21/15 | V 3543 | ||
| M025 | 46212 | I | 7520.85 | 492.02 | 557.1 | 3/21/15 | C 9724 | ||
| R007 | 46213 | I | 42.36 | 2.77 | 3.14 | 3/21/15 | X 7614 | ||
| M010 | 46214 | I | 0.4 | 0.03 | 0.03 | 3/21/15 | L 931 | ||
| H025 | 46215 | I | 6075.0 | 397.43 | 450.0 | 3/21/15 | B 7559 | ||
| M014 | 46216 | C | -1945.75 | -127.29 | -144.13 | 3/23/15 | Z 2236 | ||
| C020 | 46217 | C | -234.51 | -15.34 | -17.37 | 3/23/15 | L 4150 | ||
| T010 | 46218 | C | -5.81 | -0.38 | -0.43 | 3/23/15 | F 3362 | ||
| D014 | 46219 | C | -2500.51 | 0.0 | 0.0 | 3/23/15 | S 5178 | ||
| R025 | 46220 | C | -98.14 | -6.42 | -7.27 | 3/23/15 | Y 3634 | ||
| H014 | 46221 | C | -2500.51 | -163.58 | -185.22 | 3/23/15 | U 6004 | ||
| W020 | 46222 | C | -2447.82 | -160.14 | -181.32 | 3/23/15 | I 6582 | ||
| B010 | 46223 | C | -10.11 | -0.66 | -0.75 | 3/23/15 | S 7624 | ||
| H025 | 46224 | C | -1500.31 | -98.15 | -111.13 | 3/23/15 | M 3744 | ||
| G020 | 46225 | C | -234.51 | -15.34 | -17.37 | 3/23/15 | W 8425 | ||
| M025 | 46226 | C | -1125.14 | -73.61 | -83.34 | 3/23/15 | U 9265 | ||
| G010 | 46227 | C | -10.11 | -0.66 | -0.75 | 3/23/15 | K 3771 | ||
| R010 | 46228 | C | -7.29 | -0.48 | -0.54 | 3/23/15 | O 6430 | ||
| R007 | 46229 | C | -38.73 | -2.53 | -2.87 | 3/23/15 | Y 2310 | ||
| R020 | 46230 | C | -3145.73 | -205.8 | -233.02 | 3/23/15 | M 4849 | ||
| M010 | 46231 | C | -8.06 | -0.53 | -0.6 | 3/23/15 | E 6305 | ||
| M020 | 46232 | C | -36.03 | -2.36 | -2.67 | 3/23/15 | C 3197 | ||
| W007 | 46233 | C | -5.95 | -0.39 | -0.44 | 3/23/15 | G 5341 | ||
| W025 | 46234 | C | -73.6 | -4.81 | -5.45 | 3/23/15 | F 3953 | ||
| D025 | 46235 | C | -1500.31 | -98.15 | -111.13 | 3/23/15 | N 7018 | ||
| W025 | 46236 | I | 529.63 | 34.65 | 39.23 | 3/25/15 | I 4178 | ||
| M025 | 46237 | I | 336.47 | 22.01 | 24.92 | 3/25/15 | V 2149 | ||
| D025 | 46238 | I | 193.08 | 12.63 | 14.3 | 3/25/15 | J 1720 | ||
| K001 | 46239 | I | 212.39 | 13.89 | 15.73 | 3/25/15 | L 8320 | ||
| H025 | 46240 | I | 193.08 | 12.63 | 14.3 | 3/25/15 | Y 9604 | ||
| R025 | 46241 | I | 303.92 | 19.88 | 22.51 | 3/25/15 | F 7773 | ||
| R025 | 46242 | I | 1414.91 | 92.56 | 104.81 | 3/26/15 | Z 5601 | ||
| W025 | 46243 | I | 2602.38 | 170.25 | 192.77 | 3/26/15 | Q 666 | ||
| H014 | 46244 | I | 1245.43 | 81.48 | 92.25 | 3/27/15 | N 8872 | ||
| H025 | 46245 | I | 747.26 | 48.89 | 55.35 | 3/27/15 | C 1114 | ||
| M025 | 46246 | I | 1369.12 | 89.57 | 101.42 | 3/27/15 | M 4576 | ||
| W020 | 46247 | I | 655.02 | 42.85 | 48.52 | 3/27/15 | G 9407 | ||
| W020 | 46248 | I | 287.24 | 18.79 | 21.28 | 3/27/15 | C 4499 | ||
| W025 | 46249 | I | 2278.87 | 149.08 | 168.81 | 3/27/15 | U 3014 | ||
| R020 | 46250 | I | 651.58 | 42.63 | 48.27 | 3/27/15 | N 2910 | ||
| R025 | 46251 | I | 1243.8 | 81.37 | 92.13 | 3/27/15 | E 3930 | ||
| M014 | 46252 | I | 1707.7 | 111.72 | 126.5 | 3/27/15 | Q 4790 | ||
| R020 | 46253 | I | 592.83 | 38.78 | 43.91 | 3/27/15 | Q 4574 | ||
| H014 | 46254 | I | 1545.56 | 101.11 | 114.49 | 3/27/15 | B 5275 | ||
| M014 | 46255 | I | 549.03 | 35.92 | 40.67 | 3/27/15 | L 6290 | ||
| W025 | 46256 | I | 18349.53 | 1200.44 | 1359.22 | 3/28/15 | K 4463 | ||
| R025 | 46257 | I | 9624.71 | 629.65 | 712.94 | 3/28/15 | N 1515 | ||
| M025 | 46258 | I | 948.4 | 62.04 | 70.25 | 3/29/15 | H 7960 | ||
| D025 | 46259 | I | 747.26 | 48.89 | 55.35 | 3/29/15 | W 6235 | ||
| K001 | 46260 | I | 1020.07 | 6.67 | 7.55 | 3/29/15 | V 2940 | ||
| W025 | 46261 | I | 1511.79 | 98.9 | 111.98 | 3/29/15 | T 9329 | ||
| K001 | 46262 | I | 821.99 | 53.78 | 60.89 | 3/29/15 | Q 1071 | ||
| D025 | 46263 | I | 927.34 | 60.67 | 68.69 | 3/29/15 | I 1750 | ||
| R025 | 46264 | I | 1478.21 | 96.71 | 109.5 | 3/29/15 | L 5599 | ||
| H025 | 46265 | I | 927.34 | 60.67 | 68.69 | 3/29/15 | J 1640 | ||
| M025 | 46266 | I | 1705.61 | 111.58 | 126.34 | 3/29/15 | O 6896 | ||
| H025 | 46267 | I | 927.34 | 60.67 | 68.69 | 3/29/15 | I 1368 | ||
| W020 | 46268 | I | 46.26 | 3.03 | 3.43 | 3/30/15 | Z 3464 | ||
| M014 | 46269 | I | 407.89 | 26.68 | 30.21 | 3/30/15 | Y 294 | ||
| R010 | 46270 | I | 518.66 | 33.93 | 38.42 | 3/30/15 | D 371 | ||
| B010 | 46271 | I | 299.9 | 19.62 | 22.21 | 3/30/15 | S 7855 | ||
| D014 | 46272 | I | 321.8 | 21.05 | 23.84 | 3/30/15 | D 6275 | ||
| R008 | 46273 | I | 1485.16 | 97.16 | 110.01 | 3/30/15 | B 9751 | ||
| R020 | 46274 | I | 36.5 | 2.39 | 2.7 | 3/30/15 | R 2348 | ||
| G010 | 46275 | I | 299.9 | 19.62 | 22.21 | 3/30/15 | F 2424 | ||
| G020 | 46276 | I | 3456.2 | 226.11 | 256.01 | 3/30/15 | J 9280 | ||
| H014 | 46277 | I | 321.8 | 21.05 | 23.84 | 3/30/15 | M 2567 | ||
| M020 | 46278 | I | 4160.92 | 272.21 | 308.22 | 3/30/15 | Z 2339 | ||
| C020 | 46279 | I | 3456.2 | 226.11 | 256.01 | 3/30/15 | R 6345 | ||
| M010 | 46280 | I | 512.33 | 33.52 | 37.95 | 3/30/15 | J 6917 | ||
| T010 | 46281 | I | 886.05 | 57.97 | 65.63 | 3/30/15 | K 5604 | ||
| W008 | 46282 | I | 1787.98 | 116.97 | 132.44 | 3/30/15 | B 7778 | ||
| T007 | 46100 | P | -1684.99 | 0.0 | 0.0 | 3/31/15 | W 3699 | ON ACCOUNT | |
| T007 | 46100 | I | 5.3 | 0.35 | 0.39 | 3/31/15 | W 3699 | ||
| A123 | 46283 | P | -2000.0 | 0.0 | 0.0 | 3/31/15 | Z 6979 | ON ACCOUNT | |
| A123 | 46283 | I | 200.0 | 13.08 | 14.81 | 3/31/15 | Z 6979 | ||
| F123 | 46284 | P | -2000.0 | 0.0 | 0.0 | 3/31/15 | C 1607 | ON ACCOUNT | |
| F123 | 46284 | I | 14.71 | 0.96 | 1.09 | 3/31/15 | C 1607 | ||
| D014 | 46285 | I | 1545.56 | 101.11 | 114.49 | 3/31/15 | N 4718 | ||
| M123 | 46287 | P | -1512.18 | 0.0 | 0.0 | 3/31/15 | I 5741 | ON ACCOUNT | |
| M123 | 46287 | I | 23.0 | 1.5 | 1.7 | 3/31/15 | I 5741 | ||
| P007 | 46288 | P | -2228.55 | 0.0 | 0.0 | 3/31/15 | X 8024 | ON ACCOUNT | |
| P007 | 46288 | I | 19.99 | 1.31 | 1.48 | 3/31/15 | X 8024 | ||
| D014 | 46286 | I | 1245.43 | 81.48 | 92.25 | 4/1/15 | H 7759 |
IDEA Data Analysis Workbook/Source Files.ILB/Acc_rec2015.xls
Database
| ACCOUNT_NO | INVOICE_NO | TYPE | GROSS_AMT | GST | PST | PAID_FLAG | DATE_DATE | DATE_TIME | CUST_REF | COMMENT |
| C020 | 46000 | I | 2345.54 | 153.45 | 173.74 | 11/4/14 | 00:00:00 | A 5574 | ||
| R008 | 46001 | I | 692.72 | 45.32 | 51.31 | P | 1/4/15 | 00:00:00 | O 9844 | |
| W008 | 46002 | I | 337.61 | 22.09 | 25.01 | P | 1/4/15 | 00:00:00 | Q 7639 | |
| D014 | 46003 | I | 2306.54 | 150.90 | 170.85 | P | 1/4/15 | 00:00:00 | Z 5721 | |
| H014 | 46004 | I | 2306.54 | 150.90 | 170.85 | P | 1/4/15 | 00:00:00 | W 8122 | |
| M014 | 46005 | I | 1124.15 | 73.54 | 83.27 | P | 1/4/15 | 00:00:00 | X 2768 | |
| C020 | 46006 | I | 3039.84 | 198.87 | 225.17 | 12/5/14 | 00:00:00 | X 9574 | ||
| R005 | 46007 | I | 1971.26 | 128.96 | 146.02 | P | 1/5/15 | 00:00:00 | T 9789 | |
| G020 | 46008 | I | 2345.54 | 153.45 | 173.74 | P | 1/5/15 | 00:00:00 | S 8350 | |
| M020 | 46009 | I | 3617.01 | 236.63 | 267.93 | P | 1/5/15 | 00:00:00 | Z 5068 | |
| M123 | 46010 | I | 79.91 | 5.23 | 5.92 | 12/22/14 | 00:00:00 | V 587 | ||
| T004 | 46010 | I | 39.94 | 2.61 | 2.96 | 1/22/15 | 00:00:00 | Q 8882 | ||
| F123 | 46012 | I | 50.00 | 3.27 | 3.70 | P | 1/22/15 | 00:00:00 | K 3449 | |
| T003 | 46013 | I | 3543.54 | 231.82 | 262.48 | 1/22/15 | 00:00:00 | D 8050 | ||
| N001 | 46014 | I | 3766.58 | 246.41 | 279.01 | 1/22/15 | 00:00:00 | K 3049 | ||
| N002 | 46015 | I | 24.99 | 1.63 | 1.85 | P | 1/22/15 | 00:00:00 | C 999 | |
| F123 | 46016 | I | 2100.00 | 137.38 | 155.56 | 1/21/15 | 00:00:00 | Q 451 | ||
| A123 | 46017 | I | 50.00 | 3.27 | 3.70 | 1/22/15 | 00:00:00 | M 7134 | ||
| A123 | 46018 | I | 2100.00 | 137.38 | 155.56 | 1/22/15 | 00:00:00 | S 7316 | ||
| M123 | 46019 | I | 1975.65 | 129.25 | 146.34 | 1/22/15 | 00:00:00 | W 305 | ||
| G010 | 46020 | I | 2345.54 | 153.45 | 173.74 | 1/23/15 | 00:00:00 | L 751 | ||
| T010 | 46021 | I | 3689.05 | 241.34 | 273.26 | P | 1/23/15 | 00:00:00 | G 752 | |
| P010 | 46022 | I | 4133.88 | 270.44 | 306.21 | 1/23/15 | 00:00:00 | G 7565 | ||
| M010 | 46023 | I | 2093.15 | 136.94 | 155.05 | P | 1/23/15 | 00:00:00 | F 7554 | |
| B010 | 46024 | I | 2345.54 | 153.45 | 173.74 | P | 1/23/15 | 00:00:00 | V 5414 | |
| F123 | 46025 | I | 123.76 | 8.10 | 9.17 | 1/29/15 | 00:00:00 | C 9610 | ||
| N005 | 46026 | I | 238.37 | 15.59 | 17.66 | 1/29/15 | 00:00:00 | Z 5004 | ||
| M123 | 46027 | I | 218.62 | 14.30 | 16.19 | P | 1/29/15 | 00:00:00 | V 6578 | |
| T005 | 46028 | I | 421.09 | 27.55 | 31.19 | 1/29/15 | 00:00:00 | L 6746 | ||
| A123 | 46029 | I | 123.76 | 8.10 | 9.17 | 1/29/15 | 00:00:00 | G 7264 | ||
| F128 | 46030 | I | 2564.34 | 167.76 | 189.95 | 1/31/15 | 00:00:00 | N 4301 | ||
| P008 | 46031 | I | 283.98 | 18.58 | 21.04 | 1/31/15 | 00:00:00 | A 4754 | ||
| P008 | 46032 | I | 4848.66 | 317.20 | 359.16 | 1/31/15 | 00:00:00 | I 7053 | ||
| F128 | 46033 | I | 2361.00 | 154.46 | 174.89 | 1/31/15 | 00:00:00 | G 8569 | ||
| A128 | 46034 | I | 2361.00 | 154.46 | 174.89 | 1/31/15 | 00:00:00 | I 4686 | ||
| A128 | 46035 | I | 2564.34 | 167.76 | 189.95 | 1/31/15 | 00:00:00 | C 2311 | ||
| M128 | 46036 | I | 1311.12 | 85.77 | 97.12 | P | 1/31/15 | 00:00:00 | T 4961 | |
| M128 | 46037 | I | 4512.18 | 295.19 | 334.24 | 1/31/15 | 00:00:00 | J 625 | ||
| T008 | 46038 | I | 9266.43 | 606.22 | 686.40 | 1/31/15 | 00:00:00 | G 9699 | ||
| T008 | 46039 | I | 145.20 | 9.50 | 10.76 | P | 1/31/15 | 00:00:00 | Y 1406 | |
| R020 | 46040 | I | 218.16 | 14.27 | 16.16 | 2/4/15 | 00:00:00 | S 6291 | ||
| H025 | 46041 | I | 1383.92 | 90.54 | 102.51 | P | 2/4/15 | 00:00:00 | F 7148 | |
| M025 | 46042 | I | 156.51 | 10.24 | 11.59 | P | 2/4/15 | 00:00:00 | Z 3527 | |
| W020 | 46043 | I | 24.67 | 1.61 | 1.83 | P | 2/4/15 | 00:00:00 | H 4936 | |
| D025 | 46044 | I | 1383.92 | 90.54 | 102.51 | 2/4/15 | 00:00:00 | D 5309 | ||
| N005 | 46045 | C | -18.76 | -1.23 | -1.39 | 2/5/15 | 00:00:00 | N 2077 | WRONG PRICE | |
| M123 | 46046 | C | -64.38 | -4.21 | -4.77 | P | 2/5/15 | 00:00:00 | I 641 | WRONG PRICE |
| M010 | 46047 | I | 1180.56 | 77.23 | 87.45 | 2/5/15 | 00:00:00 | V 1548 | ||
| T010 | 46048 | I | 1590.44 | 104.05 | 117.81 | P | 2/5/15 | 00:00:00 | I 7700 | |
| A123 | 46049 | C | -78.96 | -5.17 | -5.85 | 2/5/15 | 00:00:00 | U 6881 | WRONG PRICE | |
| P010 | 46050 | I | 1026.61 | 67.16 | 76.05 | P | 2/5/15 | 00:00:00 | X 9387 | |
| G010 | 46051 | I | 762.04 | 49.85 | 56.45 | 2/5/15 | 00:00:00 | D 7733 | ||
| F123 | 46052 | C | -78.96 | -5.17 | -5.85 | 2/5/15 | 00:00:00 | T 9466 | WRONG PRICE | |
| B010 | 46053 | I | 762.04 | 49.85 | 56.45 | 2/5/15 | 00:00:00 | E 1972 | ||
| T005 | 46054 | C | -15.30 | -1.00 | -1.13 | 2/5/15 | 00:00:00 | A 5636 | WRONG PRICE | |
| W020 | 46055 | I | 2.34 | 0.15 | 0.17 | 2/7/15 | 00:00:00 | P 5595 | ||
| C020 | 46056 | I | 762.04 | 9.80 | 5.53 | P | 2/7/15 | 00:00:00 | W 3121 | |
| D014 | 46057 | I | 862.04 | 56.40 | 63.85 | P | 2/7/15 | 00:00:00 | J 4936 | |
| G020 | 46058 | I | 762.04 | 49.85 | 56.45 | 2/7/15 | 00:00:00 | I 5096 | ||
| R005 | 46059 | I | 72.15 | 4.72 | 5.34 | P | 2/7/15 | 00:00:00 | Q 8508 | |
| M020 | 46060 | I | 578.99 | 37.88 | 42.89 | P | 2/7/15 | 00:00:00 | J 6738 | |
| R008 | 46061 | I | 816.27 | 53.40 | 60.46 | 2/7/15 | 00:00:00 | E 9646 | ||
| H025 | 46062 | I | 517.22 | 33.84 | 38.31 | 2/7/15 | 00:00:00 | B 8291 | ||
| D025 | 46063 | I | 517.22 | 33.84 | 38.31 | 2/7/15 | 00:00:00 | G 9311 | ||
| W008 | 46064 | I | 752.52 | 49.23 | 55.74 | P | 2/7/15 | 00:00:00 | F 5011 | |
| M025 | 46065 | I | 70.78 | 4.63 | 5.24 | 2/7/15 | 00:00:00 | O 384 | ||
| R020 | 46066 | I | 17.12 | 1.12 | 1.27 | 2/7/15 | 00:00:00 | J 9640 | ||
| K001 | 46067 | I | 568.94 | 37.22 | 42.14 | P | 2/7/15 | 00:00:00 | T 4893 | |
| M014 | 46068 | I | 794.71 | 51.99 | 58.87 | P | 2/7/15 | 00:00:00 | A 276 | |
| W005 | 46069 | I | 54.82 | 3.59 | 4.06 | P | 2/7/15 | 00:00:00 | R 5049 | |
| H014 | 46070 | I | 862.04 | 56.40 | 63.85 | P | 2/7/15 | 00:00:00 | Y 6063 | |
| H025 | 46071 | I | 1811.10 | 118.48 | 134.16 | 2/12/15 | 00:00:00 | B 2989 | ||
| M025 | 46072 | I | 2003.34 | 131.06 | 148.40 | 2/12/15 | 00:00:00 | F 304 | ||
| H025 | 46073 | P | -2000.59 | 0.00 | 0.00 | 2/12/15 | 00:00:00 | J 2564 | ||
| H025 | 46073 | I | 47.80 | 3.13 | 3.54 | 2/12/15 | 00:00:00 | J 2564 | ||
| G010 | 46074 | I | 2018.50 | 132.05 | 149.52 | 2/12/15 | 00:00:00 | R 1827 | ||
| M025 | 46075 | P | -536.05 | 0.00 | 0.00 | 2/12/15 | 00:00:00 | V 6626 | ||
| M025 | 46075 | I | 255.60 | 16.72 | 18.93 | 2/12/15 | 00:00:00 | V 6626 | ||
| M010 | 46076 | I | 3334.34 | 218.13 | 246.99 | 2/12/15 | 00:00:00 | M 1443 | ||
| B010 | 46077 | I | 2018.50 | 132.05 | 149.52 | 2/12/15 | 00:00:00 | K 8814 | ||
| R025 | 46078 | I | 455.78 | 29.82 | 33.76 | 2/12/15 | 00:00:00 | Y 3679 | ||
| W025 | 46079 | P | -62.99 | 0.00 | 0.00 | 2/12/15 | 00:00:00 | R 3885 | ||
| W025 | 46079 | I | 44.66 | 2.92 | 3.31 | 2/12/15 | 00:00:00 | R 3885 | ||
| W025 | 46080 | I | 504.16 | 32.98 | 37.35 | P | 2/12/15 | 00:00:00 | K 8307 | |
| T010 | 46081 | I | 3851.60 | 251.97 | 285.30 | 2/12/15 | 00:00:00 | K 2826 | ||
| P010 | 46082 | I | 2331.63 | 152.54 | 172.71 | 2/12/15 | 00:00:00 | O 2089 | ||
| R025 | 46083 | P | -235.09 | 0.00 | 0.00 | 2/12/15 | 00:00:00 | Z 8449 | ||
| R025 | 46083 | I | 13.85 | 0.91 | 1.03 | 2/12/15 | 00:00:00 | Z 8448 | ||
| M014 | 46084 | I | 3735.46 | 244.38 | 276.70 | 2/16/15 | 00:00:00 | P 6650 | ||
| D025 | 46085 | I | 1811.10 | 118.48 | 134.16 | 2/16/15 | 00:00:00 | D 4340 | ||
| W011 | 46086 | I | 1810.29 | 118.43 | 134.10 | 2/16/15 | 00:00:00 | B 7448 | ||
| R011 | 46087 | I | 1462.84 | 95.70 | 108.36 | 2/16/15 | 00:00:00 | Q 9494 | ||
| H014 | 46088 | I | 3018.50 | 197.47 | 223.59 | P | 2/16/15 | 00:00:00 | F 4946 | |
| D014 | 46089 | I | 3018.50 | 197.47 | 223.59 | P | 2/16/15 | 00:00:00 | Q 4990 | |
| C020 | 46090 | I | 9018.50 | 590.00 | 668.04 | P | 2/16/15 | 00:00:00 | I 2759 | |
| K001 | 46091 | I | 1992.21 | 130.33 | 147.57 | 2/16/15 | 00:00:00 | K 6423 | ||
| M020 | 46092 | I | 8780.52 | 574.43 | 650.41 | P | 2/17/15 | 00:00:00 | B 6356 | |
| W005 | 46093 | I | 13479.34 | 881.83 | 998.47 | 2/17/15 | 00:00:00 | C 1748 | ||
| G020 | 46094 | I | 9018.50 | 590.00 | 668.04 | 2/17/15 | 00:00:00 | P 4089 | ||
| R005 | 46095 | I | 13844.67 | 905.73 | 1025.53 | 2/17/15 | 00:00:00 | D 2370 | ||
| T010 | 46096 | I | 96.52 | 6.31 | 7.15 | 2/18/15 | 00:00:00 | B 1108 | ||
| B010 | 46097 | I | 289.32 | 18.93 | 21.43 | 2/18/15 | 00:00:00 | H 1025 | ||
| C020 | 46098 | I | 289.32 | 18.93 | 21.43 | 2/18/15 | 00:00:00 | F 3582 | ||
| W011 | 46099 | I | 176.30 | 11.53 | 13.06 | P | 2/18/15 | 00:00:00 | M 3204 | |
| M020 | 46100 | I | 492.29 | 32.21 | 36.47 | P | 2/18/15 | 00:00:00 | L 3359 | |
| W005 | 46101 | I | 916.00 | 59.93 | 67.85 | 2/18/15 | 00:00:00 | W 9860 | ||
| M014 | 46102 | I | 268.65 | 17.58 | 19.90 | 2/18/15 | 00:00:00 | R 1273 | ||
| M010 | 46103 | I | 140.74 | 9.21 | 10.43 | 2/18/15 | 00:00:00 | J 8216 | ||
| P010 | 46104 | I | 198.43 | 12.98 | 14.70 | 2/18/15 | 00:00:00 | H 1539 | ||
| G010 | 46105 | I | 289.32 | 18.93 | 21.43 | 2/18/15 | 00:00:00 | U 4316 | ||
| R005 | 46106 | I | 538.33 | 35.22 | 39.88 | 2/18/15 | 00:00:00 | J 9259 | ||
| D014 | 46107 | I | 389.32 | 25.47 | 28.84 | 2/18/15 | 00:00:00 | T 8268 | ||
| G020 | 46108 | I | 289.32 | 18.93 | 21.43 | P | 2/18/15 | 00:00:00 | P 3317 | |
| D025 | 46109 | P | -2000.59 | 0.00 | 0.00 | 2/18/15 | 00:00:00 | T 8025 | ||
| D025 | 46109 | I | 20.00 | 1.31 | 1.48 | 2/18/15 | 00:00:00 | T 8025 | ||
| R011 | 46110 | I | 255.49 | 16.71 | 18.93 | 2/18/15 | 00:00:00 | J 8049 | ||
| H014 | 46111 | I | 389.32 | 25.47 | 28.84 | 2/18/15 | 00:00:00 | X 5765 | ||
| A128 | 46112 | I | 320.50 | 20.97 | 23.74 | 2/25/15 | 00:00:00 | P 5806 | ||
| T006 | 46113 | I | 2706.64 | 177.07 | 200.49 | 2/25/15 | 00:00:00 | L 798 | ||
| F128 | 46114 | I | 320.50 | 20.97 | 23.74 | 2/25/15 | 00:00:00 | I 2410 | ||
| F123 | 46115 | I | 1765.25 | 115.48 | 130.76 | 2/25/15 | 00:00:00 | Q 2700 | ||
| N005 | 46116 | I | 1412.08 | 92.38 | 104.60 | 2/25/15 | 00:00:00 | M 121 | ||
| T008 | 46117 | I | 81.91 | 5.36 | 6.07 | 2/25/15 | 00:00:00 | G 716 | ||
| M123 | 46118 | I | 3383.59 | 221.36 | 250.64 | 2/25/15 | 00:00:00 | G 5145 | ||
| M128 | 46119 | I | 285.74 | 18.69 | 21.17 | P | 2/25/15 | 00:00:00 | A 6669 | |
| A123 | 46120 | I | 1765.25 | 115.48 | 130.76 | 2/25/15 | 00:00:00 | J 1347 | ||
| P008 | 46121 | I | 91.87 | 6.01 | 6.81 | 2/25/15 | 00:00:00 | E 7040 | ||
| B010 | 46122 | I | 1028.05 | 67.26 | 76.15 | 2/27/15 | 00:00:00 | R 3791 | ||
| M010 | 46123 | I | 827.44 | 54.13 | 61.29 | 2/27/15 | 00:00:00 | S 9616 | ||
| R010 | 46124 | I | 193.76 | 12.68 | 14.35 | P | 2/27/15 | 00:00:00 | X 3814 | |
| T010 | 46125 | I | 155.95 | 10.20 | 11.55 | 2/27/15 | 00:00:00 | A 4633 | ||
| G010 | 46126 | I | 1028.05 | 67.26 | 76.15 | 2/27/15 | 00:00:00 | H 274 | ||
| Z001 | 46127 | I | 1563.61 | 102.29 | 115.82 | 3/1/15 | 00:00:00 | D 8963 | ||
| M025 | 46128 | I | 3609.43 | 236.13 | 267.37 | 3/3/15 | 00:00:00 | F 9804 | ||
| T006 | 46129 | I | 1447.32 | 94.68 | 107.21 | 3/3/15 | 00:00:00 | I 9795 | ||
| M123 | 46130 | I | 5446.64 | 356.32 | 403.45 | 3/3/15 | 00:00:00 | Q 4386 | ||
| H025 | 46131 | I | 2416.83 | 158.11 | 179.02 | 3/3/15 | 00:00:00 | N 1465 | ||
| P006 | 46132 | I | 906.14 | 59.28 | 67.12 | 3/3/15 | 00:00:00 | N 3432 | ||
| A123 | 46133 | I | 3410.05 | 223.09 | 252.60 | 3/3/15 | 00:00:00 | A 6056 | ||
| F123 | 46134 | I | 3410.05 | 223.09 | 252.60 | P | 3/3/15 | 00:00:00 | D 9712 | |
| W007 | 46135 | I | 342.06 | 22.38 | 25.34 | 3/4/15 | 00:00:00 | W 1685 | ||
| R007 | 46136 | I | 492.72 | 32.23 | 36.50 | 3/4/15 | 00:00:00 | V 2475 | ||
| C020 | 46137 | I | 1028.05 | 67.26 | 76.15 | P | 3/4/15 | 00:00:00 | U 9930 | |
| M020 | 46138 | I | 713.71 | 46.69 | 52.87 | 3/4/15 | 00:00:00 | F 491 | ||
| G020 | 46139 | I | 1028.05 | 67.26 | 76.15 | 3/4/15 | 00:00:00 | C 2446 | ||
| D014 | 46140 | I | 4028.05 | 263.52 | 298.37 | 3/4/15 | 00:00:00 | Z 3246 | ||
| R020 | 46141 | I | 901.22 | 58.96 | 66.76 | P | 3/4/15 | 00:00:00 | H 9480 | |
| M014 | 46142 | I | 1026.45 | 67.15 | 76.03 | 3/4/15 | 00:00:00 | D 9044 | ||
| H014 | 46143 | I | 4028.05 | 263.52 | 298.37 | 3/4/15 | 00:00:00 | C 2657 | ||
| W020 | 46144 | I | 229.65 | 15.02 | 17.01 | 3/4/15 | 00:00:00 | S 8587 | ||
| R025 | 46145 | I | 2961.86 | 193.77 | 219.40 | 3/5/15 | 00:00:00 | E 3320 | ||
| P009 | 46146 | I | 146.41 | 9.58 | 10.85 | 3/5/15 | 00:00:00 | L 1592 | ||
| M010 | 46147 | I | 6.15 | 0.40 | 0.46 | 3/5/15 | 00:00:00 | Q 1708 | ||
| D014 | 46148 | I | 234.51 | 15.34 | 17.37 | P | 3/5/15 | 00:00:00 | L 1382 | |
| T009 | 46149 | I | 211.26 | 13.82 | 15.65 | 3/5/15 | 00:00:00 | P 5391 | ||
| C020 | 46150 | I | 234.51 | 15.34 | 17.37 | 3/5/15 | 00:00:00 | M 5554 | ||
| M128 | 46151 | I | 768.28 | 50.26 | 56.91 | 3/5/15 | 00:00:00 | V 6983 | ||
| G020 | 46152 | I | 234.51 | 15.34 | 17.37 | 3/5/15 | 00:00:00 | E 6208 | ||
| H014 | 46153 | I | 234.51 | 15.34 | 17.37 | 3/5/15 | 00:00:00 | H 3190 | ||
| T010 | 46154 | I | 4.75 | 0.31 | 0.35 | 3/5/15 | 00:00:00 | I 657 | ||
| W025 | 46155 | I | 4423.41 | 289.38 | 327.66 | 3/5/15 | 00:00:00 | I 8272 | ||
| B010 | 46156 | I | 10.11 | 0.66 | 0.75 | 3/5/15 | 00:00:00 | F 9213 | ||
| M020 | 46157 | I | 182.69 | 11.95 | 13.53 | 3/5/15 | 00:00:00 | G 5542 | ||
| F128 | 46158 | I | 532.45 | 34.83 | 39.44 | 3/5/15 | 00:00:00 | D 8601 | ||
| W020 | 46159 | I | 1408.55 | 92.15 | 104.34 | 3/5/15 | 00:00:00 | Y 6368 | ||
| A128 | 46160 | I | 532.45 | 34.83 | 39.44 | 3/5/15 | 00:00:00 | U 1449 | ||
| R007 | 46161 | I | 53.94 | 3.53 | 4.00 | 3/5/15 | 00:00:00 | J 6864 | ||
| R020 | 46162 | I | 760.10 | 49.73 | 56.30 | 3/5/15 | 00:00:00 | Q 8983 | ||
| M014 | 46163 | I | 434.57 | 28.43 | 32.19 | 3/5/15 | 00:00:00 | W 8318 | ||
| G010 | 46164 | I | 10.11 | 0.66 | 0.75 | 3/5/15 | 00:00:00 | O 9423 | ||
| R010 | 46165 | I | 7.81 | 0.51 | 0.58 | P | 3/5/15 | 00:00:00 | R 7450 | |
| W007 | 46166 | I | 42.02 | 2.75 | 3.11 | 3/5/15 | 00:00:00 | O 4228 | ||
| D025 | 46167 | I | 2416.83 | 158.11 | 179.02 | 3/7/15 | 00:00:00 | J 1545 | ||
| K001 | 46168 | I | 2658.51 | 173.92 | 196.93 | 3/7/15 | 00:00:00 | A 5426 | ||
| D025 | 46169 | I | 140.71 | 9.21 | 10.42 | 3/7/15 | 00:00:00 | I 3289 | ||
| K001 | 46170 | I | 154.78 | 10.13 | 11.47 | 3/7/15 | 00:00:00 | W 6115 | ||
| M025 | 46171 | I | 95.95 | 6.28 | 7.11 | 3/7/15 | 00:00:00 | V 7278 | ||
| H025 | 46172 | I | 140.71 | 9.21 | 10.42 | 3/7/15 | 00:00:00 | U 7856 | ||
| W025 | 46173 | I | 37.55 | 2.46 | 2.78 | 3/9/15 | 00:00:00 | O 3020 | ||
| R025 | 46174 | I | 55.06 | 3.60 | 4.08 | 3/9/15 | 00:00:00 | A 5775 | ||
| K001 | 46210 | I | 6682.50 | 437.17 | 495.00 | 3/11/15 | 00:00:00 | H 6628 | ||
| F128 | 46175 | I | 431.80 | 28.25 | 31.99 | 3/15/15 | 00:00:00 | U 6317 | ||
| M128 | 46176 | I | 157.08 | 10.28 | 11.64 | 3/15/15 | 00:00:00 | H 8228 | ||
| P010 | 46177 | I | 286.14 | 18.72 | 21.20 | 3/15/15 | 00:00:00 | J 6696 | ||
| T010 | 46178 | I | 104.09 | 6.81 | 7.71 | 3/15/15 | 00:00:00 | B 2884 | ||
| A128 | 46179 | I | 431.80 | 28.25 | 31.99 | 3/15/15 | 00:00:00 | E 8346 | ||
| F123 | 46180 | I | 1520.98 | 99.50 | 112.67 | 3/17/15 | 00:00:00 | K 4450 | ||
| P006 | 46181 | I | 2002.03 | 130.97 | 148.30 | 3/17/15 | 00:00:00 | O 7000 | ||
| T006 | 46182 | I | 3311.46 | 216.64 | 245.29 | 3/17/15 | 00:00:00 | Z 8470 | ||
| A123 | 46183 | I | 1520.98 | 99.50 | 112.67 | 3/17/15 | 00:00:00 | V 9359 | ||
| M123 | 46184 | I | 2515.78 | 164.58 | 186.35 | 3/17/15 | 00:00:00 | A 4531 | ||
| T010 | 46185 | C | -1588.16 | -103.90 | -117.64 | 3/18/15 | 00:00:00 | I 9884 | ||
| A128 | 46186 | C | -431.00 | -28.20 | -31.93 | 3/18/15 | 00:00:00 | O 624 | ||
| B008 | 46187 | C | -431.00 | -28.20 | -31.93 | 3/18/15 | 00:00:00 | X 857 | ||
| F128 | 46188 | C | -431.00 | -28.20 | -31.93 | 3/18/15 | 00:00:00 | Z 1538 | ||
| F130 | 46189 | C | -431.00 | -28.20 | -31.93 | 3/18/15 | 00:00:00 | C 605 | ||
| T010 | 46190 | C | -1.54 | -0.10 | -0.11 | 3/18/15 | 00:00:00 | O 6826 | ||
| M128 | 46191 | C | -634.79 | -41.53 | -47.02 | 3/18/15 | 00:00:00 | W 5138 | ||
| M130 | 46192 | C | -44.45 | -2.91 | -3.29 | 3/18/15 | 00:00:00 | F 6273 | ||
| P010 | 46193 | C | -1078.31 | -70.54 | -79.87 | 3/18/15 | 00:00:00 | S 5252 | ||
| P010 | 46194 | C | -14.93 | -0.98 | -1.11 | 3/18/15 | 00:00:00 | Q 6421 | ||
| R010 | 46195 | I | 0.33 | 0.02 | 0.02 | 3/21/15 | 00:00:00 | Q 1563 | ||
| W025 | 46196 | I | 16095.58 | 1052.98 | 1192.27 | 3/21/15 | 00:00:00 | V 7610 | ||
| W020 | 46197 | I | 17307.59 | 1132.27 | 1282.04 | 3/21/15 | 00:00:00 | P 4077 | ||
| R020 | 46198 | I | 10564.84 | 691.16 | 782.58 | 3/21/15 | 00:00:00 | J 1497 | ||
| R025 | 46199 | I | 13001.28 | 850.55 | 963.06 | 3/21/15 | 00:00:00 | Q 3676 | ||
| D025 | 46200 | I | 6075.00 | 397.43 | 450.00 | 3/21/15 | 00:00:00 | Y 5531 | ||
| C020 | 46201 | I | 34.00 | 2.22 | 2.52 | 3/21/15 | 00:00:00 | R 3216 | ||
| W007 | 46202 | I | 58.34 | 3.82 | 4.32 | 3/21/15 | 00:00:00 | T 3403 | ||
| B010 | 46203 | I | 10.34 | 0.68 | 0.77 | 3/21/15 | 00:00:00 | E 9337 | ||
| D014 | 46204 | I | 10125.00 | 662.38 | 750.00 | 3/21/15 | 00:00:00 | P 6210 | ||
| T010 | 46205 | I | 0.01 | 0.00 | 0.00 | 3/21/15 | 00:00:00 | W 27 | ||
| M014 | 46206 | I | 16587.03 | 1085.13 | 1228.67 | 3/21/15 | 00:00:00 | B 2960 | ||
| M020 | 46207 | I | 46.83 | 3.06 | 3.47 | 3/21/15 | 00:00:00 | D 3128 | ||
| G010 | 46208 | I | 10.34 | 0.68 | 0.77 | 3/21/15 | 00:00:00 | Y 7937 | ||
| G020 | 46209 | I | 34.00 | 2.22 | 2.52 | 3/21/15 | 00:00:00 | A 8975 | ||
| H014 | 46211 | I | 10125.00 | 662.38 | 750.00 | 3/21/15 | 00:00:00 | V 3543 | ||
| M025 | 46212 | I | 7520.85 | 492.02 | 557.10 | 3/21/15 | 00:00:00 | C 9724 | ||
| R007 | 46213 | I | 42.36 | 2.77 | 3.14 | 3/21/15 | 00:00:00 | X 7614 | ||
| M010 | 46214 | I | 0.40 | 0.03 | 0.03 | 3/21/15 | 00:00:00 | L 931 | ||
| H025 | 46215 | I | 6075.00 | 397.43 | 450.00 | 3/21/15 | 00:00:00 | B 7559 | ||
| M014 | 46216 | C | -1945.75 | -127.29 | -144.13 | 3/23/15 | 00:00:00 | Z 2236 | ||
| C020 | 46217 | C | -234.51 | -15.34 | -17.37 | 3/23/15 | 00:00:00 | L 4150 | ||
| T010 | 46218 | C | -5.81 | -0.38 | -0.43 | 3/23/15 | 00:00:00 | F 3362 | ||
| D014 | 46219 | C | -2500.51 | 0.00 | 0.00 | 3/23/15 | 00:00:00 | S 5178 | ||
| R025 | 46220 | C | -98.14 | -6.42 | -7.27 | 3/23/15 | 00:00:00 | Y 3634 | ||
| H014 | 46221 | C | -2500.51 | -163.58 | -185.22 | 3/23/15 | 00:00:00 | U 6004 | ||
| W020 | 46222 | C | -2447.82 | -160.14 | -181.32 | 3/23/15 | 00:00:00 | I 6582 | ||
| B010 | 46223 | C | -10.11 | -0.66 | -0.75 | 3/23/15 | 00:00:00 | S 7624 | ||
| H025 | 46224 | C | -1500.31 | -98.15 | -111.13 | 3/23/15 | 00:00:00 | M 3744 | ||
| G020 | 46225 | C | -234.51 | -15.34 | -17.37 | 3/23/15 | 00:00:00 | W 8425 | ||
| M025 | 46226 | C | -1125.14 | -73.61 | -83.34 | 3/23/15 | 00:00:00 | U 9265 | ||
| G010 | 46227 | C | -10.11 | -0.66 | -0.75 | 3/23/15 | 00:00:00 | K 3771 | ||
| R010 | 46228 | C | -7.29 | -0.48 | -0.54 | 3/23/15 | 00:00:00 | O 6430 | ||
| R007 | 46229 | C | -38.73 | -2.53 | -2.87 | 3/23/15 | 00:00:00 | Y 2310 | ||
| R020 | 46230 | C | -3145.73 | -205.80 | -233.02 | 3/23/15 | 00:00:00 | M 4849 | ||
| M010 | 46231 | C | -8.06 | -0.53 | -0.60 | 3/23/15 | 00:00:00 | E 6305 | ||
| M020 | 46232 | C | -36.03 | -2.36 | -2.67 | 3/23/15 | 00:00:00 | C 3197 | ||
| W007 | 46233 | C | -5.95 | -0.39 | -0.44 | 3/23/15 | 00:00:00 | G 5341 | ||
| W025 | 46234 | C | -73.60 | -4.81 | -5.45 | 3/23/15 | 00:00:00 | F 3953 | ||
| D025 | 46235 | C | -1500.31 | -98.15 | -111.13 | 3/23/15 | 00:00:00 | N 7018 | ||
| W025 | 46236 | I | 529.63 | 34.65 | 39.23 | 3/25/15 | 00:00:00 | I 4178 | ||
| M025 | 46237 | I | 336.47 | 22.01 | 24.92 | 3/25/15 | 00:00:00 | V 2149 | ||
| D025 | 46238 | I | 193.08 | 12.63 | 14.30 | 3/25/15 | 00:00:00 | J 1720 | ||
| K001 | 46239 | I | 212.39 | 13.89 | 15.73 | 3/25/15 | 00:00:00 | L 8320 | ||
| H025 | 46240 | I | 193.08 | 12.63 | 14.30 | 3/25/15 | 00:00:00 | Y 9604 | ||
| R025 | 46241 | I | 303.92 | 19.88 | 22.51 | 3/25/15 | 00:00:00 | F 7773 | ||
| R025 | 46242 | I | 1414.91 | 92.56 | 104.81 | 3/26/15 | 00:00:00 | Z 5601 | ||
| W025 | 46243 | I | 2602.38 | 170.25 | 192.77 | 3/26/15 | 00:00:00 | Q 666 | ||
| H014 | 46244 | I | 1245.43 | 81.48 | 92.25 | 3/27/15 | 00:00:00 | N 8872 | ||
| H025 | 46245 | I | 747.26 | 48.89 | 55.35 | 3/27/15 | 00:00:00 | C 1114 | ||
| M025 | 46246 | I | 1369.12 | 89.57 | 101.42 | 3/27/15 | 00:00:00 | M 4576 | ||
| W020 | 46247 | I | 655.02 | 42.85 | 48.52 | 3/27/15 | 00:00:00 | G 9407 | ||
| W020 | 46248 | I | 287.24 | 18.79 | 21.28 | 3/27/15 | 00:00:00 | C 4499 | ||
| W025 | 46249 | I | 2278.87 | 149.08 | 168.81 | 3/27/15 | 00:00:00 | U 3014 | ||
| R020 | 46250 | I | 651.58 | 42.63 | 48.27 | 3/27/15 | 00:00:00 | N 2910 | ||
| R025 | 46251 | I | 1243.80 | 81.37 | 92.13 | 3/27/15 | 00:00:00 | E 3930 | ||
| M014 | 46252 | I | 1707.70 | 111.72 | 126.50 | 3/27/15 | 00:00:00 | Q 4790 | ||
| R020 | 46253 | I | 592.83 | 38.78 | 43.91 | 3/27/15 | 00:00:00 | Q 4574 | ||
| H014 | 46254 | I | 1545.56 | 101.11 | 114.49 | 3/27/15 | 00:00:00 | B 5275 | ||
| M014 | 46255 | I | 549.03 | 35.92 | 40.67 | 3/27/15 | 00:00:00 | L 6290 | ||
| W025 | 46256 | I | 18349.53 | 1200.44 | 1359.22 | 3/28/15 | 00:00:00 | K 4463 | ||
| R025 | 46257 | I | 9624.71 | 629.65 | 712.94 | 3/28/15 | 00:00:00 | N 1515 | ||
| M025 | 46258 | I | 948.40 | 62.04 | 70.25 | 3/29/15 | 00:00:00 | H 7960 | ||
| D025 | 46259 | I | 747.26 | 48.89 | 55.35 | 3/29/15 | 00:00:00 | W 6235 | ||
| K001 | 46260 | I | 1020.07 | 6.67 | 7.55 | 3/29/15 | 00:00:00 | V 2940 | ||
| W025 | 46261 | I | 1511.79 | 98.90 | 111.98 | 3/29/15 | 00:00:00 | T 9329 | ||
| K001 | 46262 | I | 821.99 | 53.78 | 60.89 | 3/29/15 | 00:00:00 | Q 1071 | ||
| D025 | 46263 | I | 927.34 | 60.67 | 68.69 | 3/29/15 | 00:00:00 | I 1750 | ||
| R025 | 46264 | I | 1478.21 | 96.71 | 109.50 | 3/29/15 | 00:00:00 | L 5599 | ||
| H025 | 46265 | I | 927.34 | 60.67 | 68.69 | 3/29/15 | 00:00:00 | J 1640 | ||
| M025 | 46266 | I | 1705.61 | 111.58 | 126.34 | 3/29/15 | 00:00:00 | O 6896 | ||
| H025 | 46267 | I | 927.34 | 60.67 | 68.69 | 3/29/15 | 00:00:00 | I 1368 | ||
| W020 | 46268 | I | 46.26 | 3.03 | 3.43 | 3/30/15 | 00:00:00 | Z 3464 | ||
| M014 | 46269 | I | 407.89 | 26.68 | 30.21 | 3/30/15 | 00:00:00 | Y 294 | ||
| R010 | 46270 | I | 518.66 | 33.93 | 38.42 | 3/30/15 | 00:00:00 | D 371 | ||
| B010 | 46271 | I | 299.90 | 19.62 | 22.21 | 3/30/15 | 00:00:00 | S 7855 | ||
| D014 | 46272 | I | 321.80 | 21.05 | 23.84 | 3/30/15 | 00:00:00 | D 6275 | ||
| R008 | 46273 | I | 1485.16 | 97.16 | 110.01 | 3/30/15 | 00:00:00 | B 9751 | ||
| R020 | 46274 | I | 36.50 | 2.39 | 2.70 | 3/30/15 | 00:00:00 | R 2348 | ||
| G010 | 46275 | I | 299.90 | 19.62 | 22.21 | 3/30/15 | 00:00:00 | F 2424 | ||
| G020 | 46276 | I | 3456.20 | 226.11 | 256.01 | 3/30/15 | 00:00:00 | J 9280 | ||
| H014 | 46277 | I | 321.80 | 21.05 | 23.84 | 3/30/15 | 00:00:00 | M 2567 | ||
| M020 | 46278 | I | 4160.92 | 272.21 | 308.22 | 3/30/15 | 00:00:00 | Z 2339 | ||
| C020 | 46279 | I | 3456.20 | 226.11 | 256.01 | 3/30/15 | 00:00:00 | R 6345 | ||
| M010 | 46280 | I | 512.33 | 33.52 | 37.95 | 3/30/15 | 00:00:00 | J 6917 | ||
| T010 | 46281 | I | 886.05 | 57.97 | 65.63 | 3/30/15 | 00:00:00 | K 5604 | ||
| W008 | 46282 | I | 1787.98 | 116.97 | 132.44 | 3/30/15 | 00:00:00 | B 7778 | ||
| T007 | 46100 | P | -1684.99 | 0.00 | 0.00 | 3/31/15 | 00:00:00 | W 3699 | ON ACCOUNT | |
| T007 | 46100 | I | 5.30 | 0.35 | 0.39 | 3/31/15 | 00:00:00 | W 3699 | ||
| A123 | 46283 | P | -2000.00 | 0.00 | 0.00 | 3/31/15 | 00:00:00 | Z 6979 | ON ACCOUNT | |
| A123 | 46283 | I | 200.00 | 13.08 | 14.81 | 3/31/15 | 00:00:00 | Z 6979 | ||
| F123 | 46284 | P | -2000.00 | 0.00 | 0.00 | 3/31/15 | 00:00:00 | C 1607 | ON ACCOUNT | |
| F123 | 46284 | I | 14.71 | 0.96 | 1.09 | 3/31/15 | 00:00:00 | C 1607 | ||
| D014 | 46285 | I | 1545.56 | 101.11 | 114.49 | 3/31/15 | 00:00:00 | N 4718 | ||
| M123 | 46287 | P | -1512.18 | 0.00 | 0.00 | 3/31/15 | 00:00:00 | I 5741 | ON ACCOUNT | |
| M123 | 46287 | I | 23.00 | 1.50 | 1.70 | 3/31/15 | 00:00:00 | I 5741 | ||
| P007 | 46288 | P | -2228.55 | 0.00 | 0.00 | 3/31/15 | 00:00:00 | X 8024 | ON ACCOUNT | |
| P007 | 46288 | I | 19.99 | 1.31 | 1.48 | 3/31/15 | 00:00:00 | X 8024 | ||
| D014 | 46286 | I | 1245.43 | 81.48 | 92.25 | 4/1/15 | 00:00:00 | H 7759 |
IDEA Data Analysis Workbook/Source Files.ILB/ACCPAY2015.txt
W007 Matt Cash Co 879-97 20150409 83516.79 701218 20150409 V.S.T W007 I M A Crook 97 2089.29J 20150611 14432.19 701391 20150611 HV M100 M Cash Inc UP-76409 20151003 75000 701774 20151008 HMV M100 The Cash Co Inc 784566542 20151101 59096.84 701875 20151101 V.S.T W007 F O R CASH 97 3 PMP 20151127 4580.68 701945 20151127 VST M100 Crooks Inc PI7683 20150210 21632.22 701046 20150212 H.M.V. C202 Matt Cash 51726 20150306 6242.74 701117 20150306 HMV M100 Cashonly UY-9371 20150428 2792.72 701270 20150501 HV W007 CASH & CO LD35622 20150522 25242.2 701341 20150523 HV M100 Cash Inc CS - 717 -97 20150915 75000 701728 20150917 VST W007 Crooks R Us 97 2085.29J 20151017 71409.78 701825 20151018 HMV M100 Co Cash Inc T5352 20151019 75000 701849 20151022 V.S.T M025 Luke Hair 51498 20150210 50067.31 701027 20150212 HV C202 CASH INC 13597 20150216 9264.18 701071 20150219 VST W007 Cash Inc AB 3265 M 20150319 104112.83 701166 20150325 HMV W007 The Matt Cash Co 97/3972/U 20150807 6539.64 701600 20150808 HMV C202 Only Cash BC 469904 W 20150821 5885.73 701650 20150822 HMV W007 Cash 'N' Co XQV 97 12 20150928 5319.96 701764 20151002 HV W007 Mr Cash Co 22 31 97 20151012 75000 701803 20151013 VST M100 Cash Back Inc CB3456 20151119 2922.78 701927 20151120 H.M.V. C202 F O R Cash Only MHY 1458 20150718 30850.11 701529 20150721 VST C202 P Crook .JFM.1256 20151003 78794.09 701801 20151006 HMV M100 Matt Cash & Co In879-97 20150626 190071.38 701443 20150630 HMV C202 Cash Co LD35610 20151005 13867.72 701793 20151009 HMV C202 Cash F O R Us OO P 643 20150831 49033.14 701702 20150907 HV P006 Nellie Dunn 000496CJW 20150110 64592.51 701007 20150203 H.M.V. M014 Linda Hand GR132 97 20150111 26340.3 701013 20150205 HMV M128 Maurice Mynah IN-392817 20150114 11649.19 701022 20150209 BC R025 Ray 54976/10 20150117 96166.49 701037 20150212 V.S.T F123 Dick Tate 35214DUF 20150117 13041.42 701039 20150212 HMV D014 Chimps Teaparty 147865CTR 20150117 30097.05 701042 20150212 HMV P009 P Green 566701T 20150117 29990.31 701058 20150216 BC G020 Gus Main T5727VB 20150126 5241.15 701067 20150217 VMH M128 Microcomputers 5750MCC 20150202 97376.4 701092 20150226 HMV A128 A Meadow 117- 2293 20150202 4735.39 701095 20150226 VST B008 A Raid 3501050 20150129 22880.98 701103 20150228 H.M T005 Read I.M. 47/952 20150208 80710.31 701104 20150303 BC T007 Tim Table 261199FF 20150209 37704.8 701115 20150306 HMV M025 Luke Hair 51522 20150209 86780.44 701120 20150306 HMV D025 Denise Bent 81344 20150210 696.6 701124 20150307 CW P010 Penny Cillin 100161 20150212 36147.13 701126 20150309 BC G010 Fixes 871472BUZ 20150126 6732.3 701130 20150309 HMV Z001 Miles Long FR-977 45 20150214 779.52 701139 20150312 WJT R007 Philip Upp Garage 1361/21 20150219 96240.88 701149 20150315 HMV T005 Reed I 21569 20150115 7695.32 701153 20150310 HMV K001 Joyce Tick 54655 20150223 18892.71 701163 20150320 BC W011 Witch Products 26G 20150221 99361.47 701167 20150322 BC W025 Wite Wash 2001/108.63 20150228 1685.05 701170 20150324 HMV N001 Mike Atsil AZ356 20150228 52171.73 701173 20150324 HV D025 Denise Bent 81345 20150228 21668 701175 20150324 VST P009 P Green 566737T 20150301 31819.37 701184 20150327 BC G020 Gus Main T5740VB 20150228 27273.46 701193 20150324 H.M.V. R008 Phillippa Pail 97/1261/A 20150309 50522.27 701198 20150403 HMV T005 Ronnie Biggs 7899456 20150309 57811.31 701206 20150403 HMV Z001 Wong Way 10004-97-213 20150316 6476.3 701221 20150410 H.M.V. N001 Mike Atsil AZ357 20150313 23294.64 701225 20150407 H.M.V. B010 Carter Bout 971027A 20150321 5950.71 701228 20150416 CW R008 Penny Cillin 100171 20150319 91641.27 701229 20150416 HMV W007 Trevor Wills TJ9740 20150321 14503.02 701230 20150416 WJT P007 Nellie Dunn 000512CJW 20150321 86376.13 701231 20150416 BC T010 Wanda Farr 10047 A 20150321 23563.96 701232 20150416 HMV M130 Microcomputers 5770MCC 20150324 16269.36 701240 20150418 CB C202 Cary S Matic CS - 589 -97 20150324 79217.47 701241 20150418 H.M.V. A128 A Meadow 117- 2314 20150324 8885.12 701243 20150418 BC M014 Kurt N Upp 123485 20150328 2457.94 701248 20150423 VMH M025 L Hair 263 A2Z 20150328 15151.15 701249 20150423 WJT T005 Ri Pent PPN98812 20150330 20284.39 701254 20150424 HMV F123 Dick Tate 35234DUF 20150402 645.08 701263 20150427 HV T007 Tim Table 261212FF 20150406 17354.96 701268 20150428 HMV A128 A Meadow 117- 2320 20150411 6184.65 701289 20150507 H.M.V. M014 Kurt N Upp 123489 20150413 15906.2 701294 20150508 VMH W007 Trevor Wills TJ9745 20150416 69994.18 701306 20150510 WJT F130 Farmer 14641 20150417 13206.82 701310 20150512 VMH M014 Linda Hand GR178 97 20150418 715.77 701313 20150514 HV Z001 Miles Long FR-990 65 20150420 1991.02 701318 20150515 H.M.V. H014 Honor Toze 659847 20150427 76994.34 701335 20150522 WJT H025 Ivan Aker WNZ51C 20150501 33303.69 701347 20150526 HV P009 P Green 566768T 20150507 66946.87 701357 20150531 HMV M020 Luke Hair 2883 BNA 20150507 7398.41 701359 20150531 HMV Z001 Miles Long FR-998 69 20150508 12413.11 701363 20150602 BC F130 1 Moore IN 6476 97 20150509 470.47 701365 20150604 PJM R008 Phillippa Pail 97/1288/A 20150511 49274.67 701371 20150605 V.S.T M123 Luke Hair 51594 20150511 10304.58 701372 20150605 HMV F128 Ern Payed 82 119 1 20150512 622.32 701373 20150606 VMH K001 Jackie Tupp 917375 20150508 780.82 701383 20150602 HMV K001 Joyce Tick 54701 20150515 225.06 701388 20150609 WJT P007 Nellie Dunn 000521CJW 20150522 637.93 701404 20150616 BC M130 Microcomputers 5811MCC 20150526 4697.62 701413 20150620 HV T005 Ri Pent PPN98865 20150601 19032.26 701427 20150626 BC B008 A Raid 3501087 20150602 8414.94 701429 20150627 WJT T006 Round Table BUR1364-G 20150529 18735.57 701433 20150623 BC T009 Truckstop 97 2061.29J 20150605 29339.05 701442 20150630 WJT W008 Winchester 1670 IOU-97 20150605 2373.08 701445 20150630 HMV G010 Fixes 871511BUZ 20150608 52536.79 701450 20150701 HMV F130 Farmer 14653 20150608 177.41 701451 20150703 CW W007 Trevor Wills TJ9754 20150612 5336.12 701471 20150707 H.M.V. G010 Fixes 871516BUZ 20150615 9986.12 701474 20150710 HMV R020 Polly Gunn L-1304/66 20150615 71600.4 701476 20150710 BC W007 Trevor Wills TJ9757 20150618 50519.21 701489 20150712 HMV M025 Luke Hair 2922 BNA 20150620 438.96 701497 20150716 WJT Z001 Miles Long FR-1024 81 20150622 6753.64 701501 20150717 VMH A128 A Meadow 117- 2358 20150622 27891.77 701502 20150717 VMH M128 Maurice Mynah IN-392887 20150625 32163 701505 20150720 VMH M123 Luke Hair 51611 20150619 77696.65 701510 20150714 HMV T004 Richmond IN/1163/97 20150629 1770.98 701518 20150724 BC K001 Jackie Tupp 917383 20150629 24923.54 701521 20150724 WJT M025 Joyce Tick 54727 20150702 44063.84 701526 20150727 HMV W020 Witch Products 43G 20150626 3571.32 701530 20150721 WJT Z001 Wong Way 10011-97-213 20150703 6741.05 701532 20150727 HMV W025 Wite Wash 2001/125.63 20150626 3621.38 701533 20150721 BC G010 Fixes 871531BUZ 20150704 5099.39 701538 20150730 WJT M025 Luke Hair 2923 BNA 20150704 23528.33 701543 20150730 WJT R010 Phillippa Pail 97/1320/A 20150704 50741.01 701555 20150730 HMV B010 Carter Bout 971056A 20150706 51717.96 701563 20150731 WJT M130 Microcomputers 5845MCC 20150809 63116.33 701575 20150803 HV R011 Pole Tacks LBQ-727-XY 20150714 18756.95 701595 20150808 WJT M025 Joyce Tick 54737 20150714 39639.96 701597 20150808 HV W011 Winchester 1672 IOU-97 20150714 16563.96 701602 20150808 BC T010 Wanda Farr 10098 A 20150706 20187.94 701612 20150814 WJT G010 Fixes 871539BUZ 20150720 74918.27 701613 20150814 WJT C202 Cary S Matic CS - 659 -97 20150720 4839.81 701621 20150814 BC A128 A Meadow 117- 2381 20150721 41431.33 701623 20150815 BC F130 1 Moore IN 6502 97 20150721 36418.5 701624 20150815 HMV M025 L Hair 312 A2Z 20150721 19555.36 701629 20150815 WJT F128 Ern Payed 82 157 1 20150723 9403.61 701632 20150816 WJT T005 Richmond IN/1170/97 20150725 39420.82 701639 20150819 HMV F123 Dick Tate 35276DUF 20150727 7786.03 701643 20150821 HMV T008 Tim Table 261239FF 20150728 1988.78 701648 20150822 H.M.V. W025 Wite Wash 2001/131.63 20150730 23672.29 701654 20150823 HMV R008 Penny Cillin 100227 20150724 190.61 701661 20150818 HMV G010 Fixes 871542BUZ 20150730 83645.9 701665 20150827 HMV F130 Farmer 14688 20150801 36711.04 701666 20150827 WJT R020 Polly Gunn L-1329/71 20150801 2515.3 701667 20150827 V.S.T M025 Luke Hair 2950 BNA 20150803 72319.62 701670 20150828 HMV P009 O Kay Yahs BC 469827 W 20150803 87818.28 701671 20150828 HV G020 Gus Main T5822VB 20150803 34501.96 701677 20150828 WJT H025 IM Right 4901- 97 20150807 76200.59 701689 20150901 HMV T005 Richmond IN/1171/97 20150808 299.23 701691 20150903 BC T007 Round Table BUR1377-G 20150808 16024.17 701692 20150903 H.M.V. K001 Jackie Tupp 917408 20150808 58838.39 701694 20150903 HMV W011 Winchester 1680 IOU-97 20150814 94909.33 701704 20150908 WJT Z001 Wong Way 10015-97-213 20150814 15465.78 701705 20150908 V.S.T W025 Wite Wash 2001/137.63 20150814 41948.29 701706 20150908 BC N001 Mike Atsil AZ397 20150814 1974.75 701709 20150908 VMH R010 Phillippa Pail 97/1343/A 20150824 54374.1 701734 20150918 HMV T004 Ri Pent PPN98900 20150825 69534 701738 20150919 WJT H025 IM Right 4908- 97 20150828 9059.22 701741 20150922 WJT R025 Ray 55004/10 20150829 5165.1 701745 20150924 V.S.T D025 Chimps Teaparty 147928CTR 20150903 2868.66 701750 20150927 BC M025 Joyce Tick 54789 20150903 30819.9 701751 20150927 VMH T010 Wanda Farr 10118 A 20150907 13528.85 701762 20151002 HMV M025 Luke Hair 2968 BNA 20150907 54744 701768 20151002 WJT P009 O Kay Yahs BC 469849 W 20150907 26421.25 701769 20151002 VMH F123 Denise Bent 81372 20150912 19194.58 701786 20151008 HMV W007 Trevor Wills TJ9775 20150912 44035.95 701789 20151008 HMV R008 Philip Upp Garage 1458/21 20150918 316.4 701808 20151013 H.M.V. K001 Jackie Tupp 917418 20150921 16360.27 701817 20151016 HMV F123 Dick Tate 35288DUF 20150922 58571.77 701818 20151017 WJT T009 Truckstop 97 2076.29J 20150924 6313.86 701824 20151018 HMV P008 Noah Lott 9449 NL 20151002 620.31 701852 20151026 VMH K001 Jackie Tupp 917419 20151005 76686.73 701867 20151030 HMV M025 Joyce Tick 54814 20151006 23104.1 701872 20151031 H.M.V. Z001 Wong Way 10019-97-213 20151002 16466.83 701878 20151027 HV Z001 Mike Atsil AZ410 20151010 40446.46 701882 20151105 CW H025 Ivan Aker WNZ98C 20151012 61426.43 701883 20151106 WJT B010 Carter Bout 971070A 20151012 573.91 701885 20151106 H.M G020 Fixes 871559BUZ 20151015 59798.89 701890 20151108 WJT M020 Linda Hand GR266 97 20151009 71433.11 701894 20151103 HMV M014 Kurt N Upp 123540 20151020 99834.78 701905 20151114 HMV M123 Luke Hair 51720 20151020 18008.48 701908 20151114 V.S.T T006 Ronnie Biggs 7899511 20151016 11139.21 701915 20151110 HMV T005 Richmond IN/1183/97 20151023 13355.03 701916 20151119 WJT K001 Jackie Tupp 917425 20151016 70866.39 701919 20151111 WJT R011 Pole Tacks LBQ-757-XY 20151023 64492.21 701922 20151119 BC W011 Winchester 1697 IOU-97 20151026 2698.91 701929 20151120 BC Z001 Wong Way 10022-97-213 20151026 48696.01 701930 20151120 BC N001 Microcomputers 5900MCC 20151031 3038.95 701941 20151126 HMV B008 A Meadow 117- 2411 20151102 97454.56 701944 20151127 HMV Z001 Mike Atsil AZ450 20151030 4977.15 701950 20151124 H.M.V. P007 Nellie Dunn 000564CJW 20151106 63995.56 701956 20151130 HMV G010 Farmer 14712 20151106 56203.15 701959 20151201 HMV P010 P Green 566933T 20151113 42441.89 701983 20151208 BC M025 Luke Hair 3012 BNA 20151116 12400.19 701985 20151210 H.M.V. H014 Gus Main T5872VB 20151119 3015.3 701992 20151213 VMH P008 Noah Lott 9460 NL 20151119 48274.38 701994 20151213 CW W007 Trevor Wills TJ9729 20150108 9620.16 701006 20150203 HMV G010 Fixes 871456BUZ 20150110 37418.72 701009 20150205 WJT F130 Farmer 14598 20150110 50.63 701010 20150205 HMV R011 Polly Gunn L-1221/55 20150110 6865.13 701011 20150205 HMV C202 Cary S Matic CS - 563 -97 20150112 20431.8 701017 20150207 H.M.V. F128 Ern Payed 82 64 1 20150108 18682.41 701028 20150112 HMV R007 Philip Upp Garage 1345/21 20150115 4538.39 701029 20150210 HMV N005 N Rich 9724631 20150115 53189.36 701031 20150210 BC K001 Joyce Tick 54621 20150117 7106.92 701043 20150212 WJT T007 Tim Table 261197FF 20150117 48399.72 701044 20150212 BC W020 Wite Wash 2001/102.63 20150108 35637.48 701050 20150215 V.S.T P006 Nellie Dunn 000500CJW 20150119 60539.44 701053 20150215 HMV A128 A Meadow 117- 2290 20150122 19284.31 701065 20150217 HMV M025 Luke Hair 51505 20150126 79500 701073 20150219 H.M.V. H025 Ivan Aker WNZ35C 20150126 4178.69 701078 20150221 VST F130 Farmer 14614 20150129 60355.21 701086 20150224 WJT P009 P Green 566713T 20150129 14425.71 701088 20150224 BC G020 Gus Main T5730VB 20150202 3819.33 701097 20150228 VMH T005 Ronnie Biggs 7899449 20150205 78151.51 701105 20150303 HMV T008 Truckstop 97 2044.29J 20150207 11375.26 701116 20150306 HMV W011 Witch Products 25G 20150209 21650.08 701118 20150306 HMV M025 Luke Hair 51524 20150209 18793.41 701121 20150306 BC N001 Mike Atsil AZ355 20150219 7699.6 701122 20150306 CB F130 Farmer 14624 20150210 8907.75 701131 20150309 WJT R011 Polly Gunn L-1243/58 20150210 56238.78 701132 20150309 WJT P009 P Green 566727T 20150210 41374.39 701133 20150309 BC M014 Linda Hand GR158 97 20150205 32164.91 701134 20150303 WJT P008 O Kay Yahs BC 469718 W 20150212 46487.36 701136 20150310 HMV R008 Phillippa Pail 97/1252/A 20150216 3549.45 701147 20150314 HMV F128 Ern Payed 82 95 1 20150217 79826.53 701148 20150315 WJT F123 Dick Tate 35222DUF 20150219 4603.16 701159 20150317 BC T008 Truckstop 97 2046.29J 20150223 8707.37 701165 20150321 HMV P007 Nellie Dunn 000511CJW 20150228 7988.72 701179 20150328 WJT F130 1 Moore IN 6462 97 20150226 62656.6 701192 20150324 BC R007 Philip Upp Garage 1374/21 20150309 2174.63 701201 20150403 HMV T004 Richmond IN/1151/97 20150309 79839.54 701207 20150403 WJT R025 Ray 54982/10 20150309 64073.74 701209 20150408 HMV F123 Dick Tate 35225DUF 20150310 0 701211 20150406 HMV W025 Wite Wash 2001/111.63 20150315 47203.5 701222 20150410 HV W005 Will Scarlet 323/4/QPZ/1 20150316 49277.48 701223 20150411 HMV P009 P Green 566743T 20150322 67059.93 701236 20150417 HMV M014 Linda Hand GR173 97 20150322 54107.38 701237 20150417 V.S.T R008 Phillippa Pail 97/1267/A 20150327 28842.89 701250 20150423 WJT R010 Pole Tacks LBQ-692-XY 20150403 55043.73 701265 20150428 HMV D014 Chimps Teaparty 147888CTR 20150403 2621.72 701266 20150428 CW K001 Joyce Tick 54685 20150403 41219.56 701267 20150430 HMV W011 Witch Products 32G 20150406 43407.9 701271 20150501 BC W008 Winchester 1658 IOU-97 20150406 25698.11 701272 20150501 V.S.T C202 Cary S Matic CS - 599 -97 20150410 75819.56 701287 20150507 H.M.V. Z001 Miles Long FR-985 62 20150410 29232.42 701288 20150507 HV M128 Maurice Mynah IN-392858 20150410 49060.86 701292 20150507 HV P007 Noah Lott 9391 NL 20150411 16040.01 701293 20150507 HMV R007 Philip Upp Garage 1392/21 20150412 706.55 701299 20150508 WJT N001 Mike Atsil AZ358 20150720 2396.48 701301 20150814 BC T010 Wanda Farr 10051 A 20150410 34958.34 701308 20150505 WJT M020 Luke Hair 2879 BNA 20150418 47092.73 701314 20150515 HMV C202 Cary S Matic CS - 607 -97 20150419 19182.6 701317 20150515 HMV B008 A Raid 3501077 20150417 884.74 701327 20150512 WJT R025 Ray 54988/10 20150424 13115.65 701332 20150521 HMV K001 Jackie Tupp 917363 20150424 67743.21 701333 20150521 HMV T008 Tim Table 261216FF 20150427 20148.19 701339 20150523 HMV N001 Mike Atsil AZ362 20150424 79910.95 701346 20150519 HV P007 Nellie Dunn 000517CJW 20150501 16.8 701352 20150529 H.M.V. P007 Noah Lott 9406 NL 20150510 24139.51 701368 20150605 HV M014 Kurt N Upp 123494 20150510 12694.7 701369 20150605 VMH T005 Ronnie Biggs 7899469 20150512 9105.26 701379 20150608 WJT T009 Truckstop 97 2058.29J 20150515 17955.92 701390 20150611 WJT M020 Linda Hand GR185 97 20150523 518.84 701410 20150619 HV P008 O Kay Yahs BC 469777 W 20150524 38001.45 701412 20150619 BC P007 Noah Lott 9418 NL 20150529 8142.67 701420 20150623 CW M025 L Hair 276 A2Z 20150529 41172.95 701422 20150625 BC P006 N Rich 9724724 20150601 2686.29 701428 20150627 VST Z001 Wong Way 10009-97-213 20150605 78202.6 701446 20150630 BC R020 Polly Gunn L-1298/65 20150606 8797.55 701452 20150703 HMV M123 Luke Hair 51603 20150608 17636.37 701463 20150704 HMV F128 Ern Payed 82 130 1 20150608 9114.22 701464 20150704 H.M.V. N001 Mike Atsil AZ369 20150605 61724.77 701466 20150630 VST H025 Ivan Aker WNZ67C 20150605 23665.34 701467 20150630 HV D025 Denise Bent 81359 20150605 43394.03 701468 20150630 VMH P010 P Green 566798T 20150613 77939.09 701477 20150710 V.S.T H025 Ivan Aker WNZ84C 20150615 11816.8 701485 20150711 H.M.V. B010 Carter Bout 971039A 20150616 4896.01 701487 20150712 WJT P010 P Green 566800T 20150619 12435.98 701495 20150714 WJT G020 Gus Main T5778VB 20150622 67037.75 701504 20150718 HMV M014 Kurt N Upp 123505 20150623 527.05 701507 20150720 H.M.V. T005 Ri Pent PPN98866 20150626 45528.32 701513 20150721 HMV T007 Round Table BUR1366-G 20150629 260.06 701519 20150724 HMV R025 Ray 54995/10 20150629 20110.83 701520 20150724 HMV P007 Nellie Dunn 000528CJW 20150626 145.5 701531 20150721 VST P010 P Green 566814T 20150703 4259.28 701541 20150730 WJT M020 Linda Hand GR217 97 20150703 25675.23 701542 20150730 BC P008 Noah Lott 9424 NL 20150703 3776.81 701552 20150730 VMH F128 Ern Payed 82 151 1 20150703 7646.99 701557 20150722 HMV R007 Philip Upp Garage 1421/21 20150703 79365.72 701558 20150730 HMV H025 Ivan Aker WNZ85C 20150704 40164.52 701561 20150731 HMV R008 Penny Cillin 100212 20150704 60391.48 701564 20150731 HMV W007 Trevor Wills TJ9761 20150704 3733.11 701565 20150731 BC C202 Cary S Matic CS - 651 -97 20150707 96235.53 701576 20150803 VMH A128 A Meadow 117- 2371 20150703 37054.5 701578 20150728 BC T006 Ronnie Biggs 7899482 20150713 2091.7 701588 20150808 H.M.V. F123 Dick Tate 35273DUF 20150713 38987.53 701593 20150808 H.M.V. T008 Tim Table 261238FF 20150713 22134.42 701598 20150808 H.M.V. D025 Denise Bent 81362 20150717 3999.23 701607 20150811 CW F130 Farmer 14680 20150718 16151.32 701614 20150814 WJT P010 P Green 566819T 20150718 15491.86 701616 20150814 BC M128 Maurice Mynah IN-392900 20150720 2351.9 701626 20150815 HV R007 Philip Upp Garage 1440/21 20150721 868.11 701633 20150816 WJT T007 Round Table BUR1375-G 20150724 2859.23 701640 20150820 V.S.T H014 Honor Toze 659898 20150725 21131.83 701644 20150821 HMV R011 Pole Tacks LBQ-729-XY 20150725 62589.67 701645 20150821 V.S.T M025 Joyce Tick 54761 20150727 13640.88 701647 20150822 VMH W005 Will Scarlet 336/4/QPZ/1 20150728 60258.16 701655 20150823 V.S.T H025 Ivan Aker WNZ88C 20150724 1042.41 701658 20150818 HMV M020 Linda Hand GR229 97 20150731 1526.46 701669 20150827 WJT F130 1 Moore IN 6508 97 20150803 9337.45 701676 20150828 HMV M025 Joyce Tick 54783 20150809 69932.77 701699 20150904 VMH W020 Witch Products 51G 20150812 16.64 701703 20150907 H.M.V. R008 Penny Cillin 100232 20150815 10358.39 701713 20150911 HMV W007 Trevor Wills TJ9773 20150815 33532.93 701714 20150911 V.S.T P007 Nellie Dunn 000549CJW 20150816 84054.85 701715 20150911 VMH R020 Polly Gunn L-1335/72 20150817 97835.16 701719 20150912 WJT M025 Luke Hair 2959 BNA 20150814 30200.55 701722 20150908 HMV P009 O Kay Yahs BC 469840 W 20150814 12698.08 701723 20150908 VMH M130 Microcomputers 5868MCC 20150820 97746.53 701724 20150914 VMH N005 Miles Long FR-1050 114 20150815 13170.73 701726 20150911 VMH W008 Willis T. J. JK-43-JW/97 20150907 75000 701770 20151003 BC R008 Philip Upp Garage 1446/21 20150910 62628.52 701782 20151005 H.M.V. T004 Ri Pent PPN98905 20150904 80737.13 701783 20150929 WJT B010 Carter Bout 971061A 20150911 30536.55 701787 20151008 HMV G010 Fixes 871550BUZ 20150912 7693.74 701792 20151009 HMV M025 Luke Hair 2969 BNA 20150915 41644.48 701797 20151012 WJT P009 O Kay Yahs BC 469857 W 20150915 25519.67 701798 20151012 VMH F130 Ern Payed 82 185 1 20150918 55375.7 701807 20151013 BC T006 Ronnie Biggs 7899500 20150919 78605.73 701813 20151016 HMV W011 Winchester 1687 IOU-97 20150924 17129.56 701827 20151019 HMV M123 Luke Hair 51701 20150925 66988.58 701829 20151020 WJT F123 Denise Bent 81373 20150925 29188.2 701832 20151022 HMV B010 Carter Bout 971068A 20150925 66046.21 701833 20151022 HMV P007 Nellie Dunn 000562CJW 20150925 50547.23 701836 20151022 CW H014 Ian Till 4821- 97 20150926 72290.81 701839 20151023 HV M130 Microcomputers 5892MCC 20150928 18177.06 701845 20151024 CW M014 Kurt N Upp 123537 20150925 56896.72 701853 20151020 WJT M025 L Hair 342 A2Z 20150925 66365.59 701854 20151020 HMV F130 Ern Payed 82 196 1 20151003 50907.75 701857 20151030 BC R025 Ray 55009/10 20151003 29616.37 701866 20151030 WJT F123 Dick Tate 35292DUF 20151003 19397.27 701868 20151030 WJT R011 Pole Tacks LBQ-755-XY 20151005 53309.77 701870 20151030 HMV T008 Tim Table 261256FF 20151006 10649.17 701873 20151101 WJT W011 Winchester 1692 IOU-97 20151002 84737.41 701877 20151027 HV W008 Trevor Wills TJ9780 20151013 88915.38 701887 20151108 BC P007 Nellie Dunn 000563CJW 20151013 546.43 701888 20151108 H.M.V. P010 P Green 566881T 20151008 89907.27 701893 20151103 H.M.V. M025 Luke Hair 2990 BNA 20151015 19407.56 701895 20151109 H.M.V. D014 Cary S Matic CS - 698 -97 20151016 1138.78 701898 20151110 BC B008 A Meadow 117- 2404 20151016 28650.9 701900 20151110 BC H014 Gus Main T5856VB 20151017 10023.66 701902 20151113 VMH P008 Noah Lott 9452 NL 20151019 17777.64 701904 20151114 VMH R010 Phillippa Pail 97/1371/A 20151019 40697.41 701907 20151114 V.S.T B008 A Raid 3501124 20151020 14288.57 701913 20151116 BC T007 Round Table BUR1390-G 20151022 87217.32 701917 20151116 HMV R025 Ray 55012/10 20151016 61538.44 701918 20151110 HMV F123 Dick Tate 35296DUF 20151016 43000.92 701920 20151110 WJT W005 Will Scarlet 351/4/QPZ/1 20151026 8691.83 701932 20151120 BC R020 Polly Gunn L-1366/78 20151106 5332.03 701960 20151201 WJT F123 Denise Bent 81382 20151106 5934.18 701969 20151203 WJT M020 Linda Hand GR286 97 20151113 38790.82 701984 20151210 HMV M128 Maurice Mynah IN-392943 20151119 6146.89 701993 20151213 H.M.V. D025 Denise Bent 81340 20150105 75373.66 701003 20150202 BC P010 Penny Cillin 100139 20150107 37754.37 701005 20150203 HMV F130 1 Moore IN 6428 97 20150112 78262.98 701020 20150209 CW G020 Gus Main T5721VB 20150112 14370.05 701021 20150209 VMH P007 Noah Lott 9358 NL 20150115 48911.96 701023 20150212 WJT M014 Kurt N Upp 123456 20150115 8072.34 701024 20150212 WJT R008 Phillippa Pail 97/1238/A 20150111 39245.38 701026 20150212 HMV T005 Ri Pent PPN98765 20150114 57170.89 701030 20150210 WJT B008 A Raid 3501040 20150114 38149.16 701032 20150210 H.V. T005 Ronnie Biggs 7899446 20150114 16281.75 701034 20150210 WJT F123 Edward Zoff 99799ABC 20150117 2713.16 701052 20150115 HMV G010 Fixes 871463BUZ 20150119 3685.75 701055 20150216 WJT F130 Farmer 14604 20150119 21053.24 701056 20150216 HMV F130 1 Moore IN 6438 97 20150121 40325.22 701066 20150217 CW M128 Maurice Mynah IN-392830 20150122 33607.97 701068 20150219 CB R008 Phillippa Pail 97/1247/A 20150122 86117.39 701072 20150219 BC B010 Carter Bout 971005A 20150125 16048.24 701080 20150221 VMH G010 Fixes 871465BUZ 20150128 9351.19 701085 20150224 WJT Z001 Miles Long FR-972 42 20150129 3649.83 701094 20150226 HMV F130 1 Moore IN 6446 97 20150129 24163.78 701096 20150226 CW M025 Luke Hair 51516 20150202 76855.73 701098 20150228 H.M.V. F128 Ern Payed 82 84 1 20150202 2338.22 701099 20150228 WJT A128 A Meadow 117- 2299 20150115 1428.23 701140 20150312 VST G020 Gus Main T5732VB 20150115 5941.66 701142 20150312 HMV M025 L Hair 232 A2Z 20150214 5033.83 701146 20150313 HMV R025 Ray 54980/10 20150212 1861.26 701157 20150310 H.M.V. W008 Winchester 1652 IOU-97 20150226 68196.81 701168 20150324 WJT M014 Linda Hand GR162 97 20150228 66083.48 701185 20150327 V.S.T P008 O Kay Yahs BC 469730 W 20150301 61190.89 701187 20150329 WJT C202 Cary S Matic CS - 586 -97 20150301 89607.29 701189 20150329 H.M.V. Z001 Miles Long FR-981 55 20150305 27856.65 701190 20150331 BC R010 Pole Tacks LBQ-691-XY 20150309 54682.48 701213 20150406 HMV D014 Chimps Teaparty 147878CTR 20150305 18738.66 701214 20150331 HMV K001 Joyce Tick 54669 20150312 193487.22 701215 20150407 HMV P008 O Kay Yahs BC 469743 W 20150321 65444.28 701239 20150417 BC G020 Gus Main T5746VB 20150323 7388.62 701245 20150419 HMV F128 Ern Payed 82 108 1 20150328 41916.29 701252 20150424 HV P006 N Rich 9724673 20150329 49753.9 701255 20150425 BC W005 Will Scarlet 329/4/QPZ/1 20150406 72475.35 701275 20150502 BC P007 Nellie Dunn 000513CJW 20150402 48672.02 701277 20150428 CB F130 Farmer 14639 20150409 3151.97 701280 20150505 HV G020 Gus Main T5755VB 20150410 27589.03 701291 20150507 HMV R008 Phillippa Pail 97/1274/A 20150411 27125.44 701296 20150508 WJT R008 Penny Cillin 100176 20150412 26701.63 701305 20150509 WJT R011 Polly Gunn L-1271/62 20150417 27540.23 701311 20150514 HMV F130 1 Moore IN 6474 97 20150418 58626.59 701320 20150515 PJM T005 Ri Pent PPN98842 20150416 841.8 701325 20150512 HMV R010 Pole Tacks LBQ-702-XY 20150424 43867.99 701336 20150522 BC T010 Wanda Farr 10059 A 20150501 2758.74 701353 20150529 V.S.T F130 Farmer 14651 20150503 3528.29 701355 20150530 VMH P008 O Kay Yahs BC 469767 W 20150430 6434.24 701360 20150526 H.M.V. M130 Microcomputers 5799MCC 20150507 53618.16 701361 20150602 BC C202 Cary S Matic CS - 613 -97 20150507 17168.51 701362 20150602 HMV A128 A Meadow 117- 2333 20150507 8694.55 701364 20150602 CW M025 L Hair 270 A2Z 20150509 51211.19 701370 20150605 HMV R007 Philip Upp Garage 1394/21 20150510 7437.91 701374 20150606 WJT T005 Ri Pent PPN98850 20150510 8914.29 701375 20150606 HMV R025 Ray 54990/10 20150507 22724.88 701382 20150602 HMV F123 Dick Tate 35248DUF 20150507 50898.84 701384 20150602 VMH D025 Chimps Teaparty 147895CTR 20150514 989.94 701387 20150609 BC D025 Denise Bent 81356 20150518 27389.59 701400 20150615 HV T010 Wanda Farr 10065 A 20150522 393.2 701405 20150618 H.M.V. G010 Fixes 871505BUZ 20150522 1537.26 701406 20150618 H.M.V. R020 Polly Gunn L-1287/64 20150522 77328.4 701408 20150618 HMV M128 Maurice Mynah IN-392875 20150529 10534.35 701419 20150623 VST T005 Ronnie Biggs 7899475 20150601 5232.23 701431 20150629 V.S.T T004 Richmond IN/1161/97 20150601 27547.05 701432 20150629 HMV R010 Pole Tacks LBQ-714-XY 20150529 8073.53 701438 20150623 HMV W025 Wite Wash 2001/123.63 20150605 51122.83 701447 20150702 HMV Z001 Miles Long FR-1010 75 20150608 49225.03 701459 20150704 CW R007 Philip Upp Garage 1413/21 20150608 11008.05 701465 20150706 HMV B010 Carter Bout 971037A 20150613 23422.28 701469 20150710 HMV P007 Nellie Dunn 000524CJW 20150611 3332.74 701472 20150707 HV T010 Wanda Farr 10072 A 20150612 13281.74 701473 20150809 H.M.V. M025 Luke Hair 2919 BNA 20150612 9197.83 701479 20150710 V.S.T C202 Cary S Matic CS - 624 -97 20150613 2656.45 701482 20150710 VMH N001 Mike Atsil AZ387 20150716 18100.3 701484 20150811 VMH R008 Penny Cillin 100201 20150615 70348.7 701488 20150712 WJT T010 Wanda Farr 10076 A 20150619 3666.7 701491 20150717 HMV P008 O Kay Yahs BC 469799 W 20150619 28072.51 701498 20150716 VST M130 Microcomputers 5837MCC 20150619 17344.95 701499 20150716 VMH F130 1 Moore IN 6487 97 20150620 18570.19 701503 20150717 VMH P006 N Rich 9724743 20150625 944.91 701514 20150721 VMH Truckstop 97 2064.29J 20150625 16318.97 701528 20150721 V.S.T P008 O Kay Yahs BC 469813 W 20150703 40609.31 701544 20150730 VMH F130 1 Moore IN 6493 97 20150703 54155.77 701549 20150730 WJT M128 Maurice Mynah IN-392898 20150703 1572.21 701551 20150730 HMV D025 Denise Bent 81361 20150703 45972.65 701562 20150731 VMH T010 Wanda Farr 10088 A 20150703 39132.42 701567 20150731 HMV F130 Farmer 14677 20150703 8384.5 701569 20150731 HMV P010 P Green 566817T 20150704 19647.86 701571 20150731 HMV M020 Linda Hand GR223 97 20150706 79002.68 701572 20150801 HMV M025 Luke Hair 2938 BNA 20150706 74054.8 701573 20150801 HMV P009 O Kay Yahs BC 469817 W 20150706 5676.82 701574 20150803 HMV F130 1 Moore IN 6497 97 20150809 74935.34 701579 20150804 BC T007 Round Table BUR1370-G 20150712 21598.62 701590 20150808 WJT H014 Honor Toze 659888 20150712 60341.35 701594 20150808 BC W020 Witch Products 47G 20150712 8228.56 701601 20150808 V.S.T B010 Carter Bout 971057A 20150716 5302.61 701608 20150811 WJT M020 Linda Hand GR227 97 20150717 71653.36 701617 20150814 HMV R010 Phillippa Pail 97/1321/A 20150720 15947.69 701630 20150815 BC F128 Edward Zoff 99827ABC 20150727 53088.1 701656 20150823 VMH N001 Mike Atsil AZ394 20150723 32433 701657 20150818 HV W007 Trevor Wills TJ9769 20150730 10959.33 701662 20150825 WJT A128 A Meadow 117- 2387 20150801 11729.85 701675 20150828 BC M128 Maurice Mynah IN-392912 20150803 51108.17 701678 20150829 HV F128 Ern Payed 82 162 1 20150730 29527.27 701684 20150825 WJT R025 Ray 55001/10 20150807 32895.41 701693 20150903 WJT H014 Honor Toze 659904 20150807 89987.81 701696 20150904 HMV T008 Tim Table 261241FF 20150814 28401.09 701700 20150911 HMV T009 Truckstop 97 2072.29J 20150810 27182.07 701701 20150907 HMV W005 Will Scarlet 343/4/QPZ/1 20150812 12097.78 701707 20150908 V.S.T F128 Edward Zoff 99828ABC 20150812 68656.55 701708 20150908 CW H025 Ivan Aker WNZ89C 20150814 5913.96 701710 20150910 HMV C202 Cary S Matic CS - 675 -97 20150812 25960.81 701725 20150908 BC G020 Gus Main T5828VB 20150821 5325.14 701729 20150917 WJT M123 Luke Hair 51673 20150823 4690.78 701735 20150919 HMV P006 N Rich 9724806 20150824 32502.53 701739 20150920 VMH T005 Richmond IN/1176/97 20150828 3120.83 701743 20150924 HMV F123 Dick Tate 35284DUF 20150829 76992.02 701747 20150925 HMV H014 Honor Toze 659917 20150829 17174.58 701748 20150925 WJT W025 Wite Wash 2001/139.63 20150904 43317.73 701758 20150928 HMV F128 Edward Zoff 99833ABC 20150904 2546.59 701760 20151002 H.M.V. C202 Cary S Matic CS - 678 -97 20150907 23209.27 701771 20151003 BC A128 A Meadow 117- 2393 20150907 75777.62 701773 20151003 BC P008 Noah Lott 9442 NL 20150907 11940.05 701777 20151005 VMH C202 Cary S Matic CS - 681 -97 20150914 83160.52 701800 20151012 BC P006 N Rich 9724807 20150918 15877.15 701810 20151015 VMH H014 Honor Toze 659924 20150920 15658.95 701819 20151017 WJT M025 Joyce Tick 54803 20150921 30248.48 701822 20151018 CW T008 Tim Table 261250FF 20150921 94868.35 701823 20151018 HMV W005 Wanda Farr 10125 A 20150925 47353.89 701837 20151022 BC M025 Luke Hair 2984 BNA 20150926 24072.05 701843 20151023 V.S.T G020 Gus Main T5850VB 20150924 57716.72 701850 20151020 HV R008 Philip Upp Garage 1459/21 20150924 6586.88 701858 20151020 HMV F128 Edward Zoff 99838ABC 20151008 13875.31 701881 20151103 HMV F123 Denise Bent 81380 20151010 94058.95 701884 20151106 WJT G010 Farmer 14697 20151008 18700.53 701891 20151103 HMV R008 Philip Upp Garage 1464/21 20151018 7973.99 701910 20151114 BC F130 Ern Payed 82 211 1 20151101 1610.73 701948 20151128 WJT R008 Philip Upp Garage 1470/21 20151102 3435.15 701949 20151129 HMV R008 Penny Cillin 100264 20151029 22093.3 701954 20151124 H.M.V. D014 Cary S Matic CS - 709 -97 20151105 40732.77 701966 20151201 HMV R008 Penny Cillin 100273 20151106 24004.54 701971 20151204 H.M.V. P007 Nellie Dunn 000566CJW 20151106 432.34 701975 20151204 HMV M014 Kurt N Upp 123547 20151116 41632.87 701995 20151213 BC M025 L Hair 363 A2Z 20151031 48087.15 701996 20151215 WJT H025 Ivan Aker WNZ28C 20150104 79571.88 701002 20150202 BC T009 Wanda Farr 10000 A 20150106 4522.28 701008 20150203 BC M128 Microcomputers 5731MCC 20150110 73703.93 701016 20150228 HMV T005 Reid IM KN216JP 20150113 18228.91 701033 20150210 HMV T004 Richmond IN/1145/97 20150114 47438.23 701035 20150212 HMV K001 Jackie Tupp 917328 20150114 38309.89 701038 20150212 VMH H014 Honor Toze 659813 20150114 9474.74 701040 20150212 CW R010 Pole Tacks LBQ-684-XY 20150114 11129.65 701041 20150212 H.M.V. W011 Witch Products 21G 20150115 29033.8 701047 20150212 HMV M128 Microcomputers 5745MCC 20150111 17304.8 701062 20150206 HMV M014 Kurt N Upp 123461 20150121 68262.53 701070 20150219 WJT F128 Ern Payed 82 76 1 20150122 54881.69 701074 20150219 WJT P006 Nellie Dunn 000504CJW 20150125 24308.55 701083 20150223 HMV M020 Luke Hair 2850 BNA 20150128 3593.3 701090 20150226 V.S.T N005 N Rich 9724644 20150202 50123.84 701102 20150228 CB T004 Richmond IN/1146/97 20150203 92390.9 701106 20150303 HMV R010 Pole Tacks LBQ-687-XY 20150203 88748.91 701112 20150303 HMV D014 Chimps Teaparty 147870CTR 20150205 1197.58 701113 20150305 HMV K001 Joyce Tick 54640 20150205 45492.03 701114 20150306 HMV M128 Microcomputers 5753MCC 20150210 2590.3 701137 20150310 WJT P007 Noah Lott 9370 NL 20150115 9565.97 701144 20150313 CB T005 Ronnie Biggs 7899450 20150223 50345.19 701154 20150323 BC D014 Chimps Teaparty 147873CTR 20150219 91635.74 701162 20150319 VMH F123 Edward Zoff 99800ABC 20150224 1484.63 701172 20150324 BC P010 Penny Cillin 100170 20150226 84795.24 701177 20150326 HMV W007 Trevor Wills TJ9739 20150226 53520.08 701178 20150328 HMV M020 Luke Hair 2856 BNA 20150228 65013.64 701186 20150328 H.M.V. M025 Luke Hair 51547 20150306 24393.13 701199 20150403 HMV P006 N Rich 9724664 20150306 19238.73 701203 20150403 H.M.V. T005 Ried I.M. GHO 975 20150306 32680.67 701205 20150403 V.S.T T006 Round Table BUR1354-G 20150306 12854.41 701208 20150403 V.S.T T008 Truckstop 97 2048.29J 20150312 51554.92 701217 20150409 BC F130 Farmer 14636 20150319 2856.94 701234 20150416 HMV Z001 Miles Long FR-983 58 20150321 15197.54 701242 20150418 CB M128 Maurice Mynah IN-392848 20150317 48421.08 701246 20150414 BC H014 IM Right 4835- 97 20150328 32956.22 701257 20150425 VMH T005 Ronnie Biggs 7899459 20150328 43195.93 701258 20150425 HMV T004 Richmond IN/1155/97 20150329 24452.9 701259 20150427 V.S.T T006 Round Table BUR1355-G 20150329 79839.65 701260 20150427 H.M.V. R025 Ray 54985/10 20150329 64995.54 701261 20150427 BC K001 Jackie Tupp 917356 20150324 79990.86 701262 20150421 H.M.V. H014 Honor Toze 659840 20150331 2953.29 701264 20150428 HMV M020 Luke Hair 2872 BNA 20150409 41563.48 701284 20150507 BC M130 Microcomputers 5776MCC 20150409 5334.93 701286 20150507 HV H025 Ivan Aker WNZ45C 20150411 21984.94 701302 20150509 VST D025 Denise Bent 81350 20150411 65761.63 701303 20150509 HMV B010 Carter Bout 971028A 20150411 52264.16 701304 20150509 H.M.V. P007 Nellie Dunn 000516CJW 20150412 92231.51 701307 20150510 HV M130 Microcomputers 5791MCC 20150417 68752.26 701316 20150515 H.M.V. A128 A Meadow 117- 2323 20150417 19935.19 701319 20150515 CW F128 Ern Payed 82 115 1 20150419 375.72 701323 20150517 VMH R007 Philip Upp Garage 1393/21 20150414 62795.19 701324 20150512 V.S.T B010 Carter Bout 971034A 20150430 2480.49 701349 20150529 HMV W007 Trevor Wills TJ9749 20150430 56175.65 701351 20150529 V.S.T G010 Fixes 871504BUZ 20150501 31571.3 701354 20150529 CW R011 Polly Gunn L-1282/63 20150503 55802.44 701356 20150531 BC H014 IM Right 4861- 97 20150509 1868.56 701378 20150606 VMH T004 Richmond IN/1159/97 20150510 28889.45 701380 20150608 H.M.V. H014 Honor Toze 659860 20150512 9149.07 701385 20150609 WJT R010 Pole Tacks LBQ-709-XY 20150512 18651.62 701386 20150609 HMV W005 Will Scarlet 330/4/QPZ/1 20150515 47078.32 701396 20150612 HMV R008 Penny Cillin 100191 20150519 17614.61 701402 20150616 V.S.T F130 Farmer 14652 20150521 66649.45 701407 20150618 VMH M025 Luke Hair 2896 BNA 20150522 90839.5 701411 20150619 WJT F130 1 Moore IN 6486 97 20150519 590.17 701417 20150616 PJM R008 Phillippa Pail 97/1292/A 20150529 3752.46 701423 20150626 WJT F128 Ern Payed 82 121 1 20150529 67288.5 701425 20150626 CW K001 Jackie Tupp 917382 20150526 33936.8 701435 20150623 HMV K001 Joyce Tick 54714 20150602 33641.9 701440 20150630 WJT F128 Edward Zoff 99809ABC 20150605 30020.53 701449 20150703 VMH M130 Microcomputers 5823MCC 20150606 8247.21 701457 20150704 CW C202 Cary S Matic CS - 623 -97 20150606 1573.18 701458 20150704 HMV Z001 Wong Way 10023-97-213 20150606 10633.49 701461 20150704 HMV M020 Linda Hand GR196 97 20150612 92274.62 701478 20150710 WJT P008 O Kay Yahs BC 469784 W 20150612 14759.29 701480 20150710 CW D025 Denise Bent 81360 20150613 4898.43 701486 20150711 VMH M020 Linda Hand GR206 97 20150618 72433.51 701496 20150716 HMV C202 Cary S Matic CS - 634 -97 20150619 10671.81 701500 20150717 HMV M025 L Hair 289 A2Z 20150622 51124.58 701508 20150720 HMV R007 Philip Upp Garage 1417/21 20150623 5702.62 701512 20150721 BC B008 A Raid 3501091 20150623 13147.16 701515 20150721 WJT F123 Dick Tate 35266DUF 20150626 29982.12 701522 20150724 CW D025 Chimps Teaparty 147911CTR 20150627 7175.52 701525 20150725 HMV T008 Tim Table 261231FF 20150629 0 701527 20150727 H.M.V. F128 Edward Zoff 99818ABC 20150630 4335.61 701535 20150728 VMH R020 Polly Gunn L-1321/68 20150702 29626 701540 20150730 HMV N005 Miles Long FR-1026 82 20150702 79306.51 701547 20150730 HMV G020 Gus Main T5789VB 20150702 1785.12 701550 20150730 BC M123 Luke Hair 51617 20150702 2949.42 701556 20150730 WJT P007 Nellie Dunn 000538CJW 20150703 63941.16 701566 20150731 HMV G010 Fixes 871538BUZ 20150703 72809.45 701568 20150731 WJT N005 Miles Long FR-1035 91 20150706 10941.25 701577 20150803 HV H025 IM Right 4889- 97 20150710 12279.42 701587 20150807 HMV R025 Ray 54997/10 20150711 7010.41 701591 20150808 HMV D025 Chimps Teaparty 147914CTR 20150711 6242.65 701596 20150808 H.M.V. N001 Mike Atsil AZ389 20150712 4641.13 701605 20150809 HV W007 Trevor Wills TJ9766 20150714 93254.75 701610 20150811 HMV P007 Nellie Dunn 000542CJW 20150714 25884.91 701611 20150811 HV M025 Luke Hair 2941 BNA 20150717 78064.15 701618 20150814 BC M014 Kurt N Upp 123513 20150718 79902.9 701628 20150815 HMV M123 Luke Hair 51655 20150720 83.39 701631 20150816 H.M.V. P006 N Rich 9724777 20150720 36734.81 701635 20150816 HV T009 Truckstop 97 2070.29J 20150725 720.54 701649 20150822 H.M.V. D025 Denise Bent 81366 20150721 26048.07 701659 20150818 H.M.V. M123 Luke Hair 51667 20150728 18677.43 701683 20150825 WJT B008 A Raid 3501108 20150804 36939.75 701688 20150901 BC D025 Chimps Teaparty 147925CTR 20150807 785.91 701698 20150904 HMV D025 Denise Bent 81367 20150812 38662.44 701711 20150910 HMV P010 P Green 566853T 20150816 8961.88 701720 20150913 HMV A128 A Meadow 117- 2391 20150818 28857.72 701727 20150915 BC M014 Kurt N Upp 123522 20150821 79765.18 701732 20150918 WJT R008 Philip Upp Garage 1443/21 20150822 19773.18 701737 20150919 V.S.T B008 A Raid 3501118 20150818 20248.35 701740 20150915 BC T006 Ronnie Biggs 7899499 20150825 75243 701742 20150922 HMV T009 Truckstop 97 2073.29J 20150825 36382.04 701753 20150922 BC W011 Winchester 1684 IOU-97 20150903 37322.67 701756 20150928 V.S.T W005 Will Scarlet 344/4/QPZ/1 20150903 1072.46 701759 20150928 V.S.T P007 Nellie Dunn 000554CJW 20150904 35361.76 701761 20151002 VMH M014 Kurt N Upp 123532 20150907 74182.01 701777 20151005 WJT M025 L Hair 339 A2Z 20150907 4748.13 701778 20151005 BC H025 Ivan Aker WNZ92C 20150908 18785.53 701785 20151006 WJT P010 P Green 566862T 20150911 81068.78 701795 20151009 WJT T005 Richmond IN/1177/97 20150918 26476.44 701814 20151016 HMV T007 Round Table BUR1384-G 20150918 25.77 701815 20151016 HMV W020 Witch Products 54G 20150920 29463.41 701826 20151018 BC M123 Luke Hair 51690 20150922 35179.83 701828 20151020 HMV H025 Ivan Aker WNZ97C 20150924 98990.25 701831 20151022 WJT G020 Fixes 871552BUZ 20150925 76758.03 701838 20151023 WJT P010 P Green 566869T 20150925 57247.56 701841 20151023 V.S.T M020 Linda Hand GR264 97 20150925 17510.96 701842 20151023 WJT M128 Maurice Mynah IN-392937 20150922 24939.3 701851 20151020 VMH R010 Phillippa Pail 97/1361/A 20150922 6249.12 701855 20151020 WJT P006 N Rich 9724813 20150929 1831.14 701860 20151027 VMH H025 IM Right 4920- 97 20150929 2546.27 701862 20151027 WJT D025 Chimps Teaparty 147936CTR 20151003 49162.23 701871 20151031 CB P009 O Kay Yahs BC 469880 W 20151006 36653.95 701896 20151103 H.M.V. M130 Microcomputers 5897MCC 20151013 43018.86 701897 20151110 H.M.V. N005 Miles Long FR-1065 137 20151013 84188.29 701899 20151110 H.M.V. M128 Maurice Mynah IN-392939 20151016 10901.67 701903 20151113 CW F130 Ern Payed 82 200 1 20151017 60435.75 701909 20151114 BC T004 Ri Pent PPN98930 20151017 60044.38 701911 20151114 BC W025 Wite Wash 2001/144.63 20151023 2526.55 701931 20151120 WJT G020 Fixes 871560BUZ 20151024 35462.59 701934 20151121 WJT R020 Polly Gunn L-1364/77 20151026 88128.54 701936 20151122 HMV M123 Luke Hair 51723 20151031 7582.46 701947 20151128 H.M.V. F123 Denise Bent 81381 20151027 43738.1 701952 20151124 WJT B010 Carter Bout 971071A 20151027 6302.26 701953 20151124 H.M R008 Penny Cillin 100285 20151106 27882.82 701973 20151204 HMV W005 Wanda Farr 10139 A 20151106 67889.29 701976 20151204 WJT W008 Trevor Wills TJ9788 20151107 15301.09 701977 20151205 BC G020 Fixes 871567BUZ 20151108 43159.24 701980 20151206 HV P009 O Kay Yahs BC 469903 W 20151113 9707.03 701986 20151211 HMV N001 Microcomputers 5918MCC 20151113 58986.66 701987 20151211 HMV D014 Cary S Matic CS - 712 -97 20151114 30101.08 701988 20151212 CW F130 1 Moore IN 6520 97 20151116 51482.26 701991 20151213 HMV R010 Phillippa Pail 97/1372/A 20151119 205.49 702001 20151229 H.M.V. P008 O Kay Yahs BC 469701 W 20150108 11001.66 701015 20150207 H.M.V. Z001 Miles Long FR-963 32 20150110 8579.04 701018 20150208 HMV F130 A Meadow 117- 2287 20150110 34259.55 701019 20150209 HMV R011 Polly Gunn L-1232/56 20150117 30457.59 701057 20150216 HMV P008 O Kay Yahs BC 469703 W 20150112 32293.4 701061 20150216 HMV C202 Cary S Matic CS - 564 -97 20150119 25943.21 701063 20150217 H.M.V. P007 Noah Lott 9359 NL 20150120 83880.73 701069 20150219 BC R007 Philip Upp Garage 1351/21 20150121 1353.14 701075 20150219 HMV T005 Ri Pent PPN98778 20150121 32169.7 701076 20150219 WJT N001 Mike Atsil AZ279 20150114 39900.83 701077 20150219 BC W007 Trevor Wills TJ9732 20150124 74625.32 701082 20150223 BC R011 Polly Gunn L-1236/57 20150126 42026.88 701087 20150224 WJT M014 Linda Hand GR150 97 20150126 56227.23 701089 20150224 WJT C202 Cary S Matic CS - 571 -97 20150128 2957.2 701093 20150226 H.M.V. R007 Philip Upp Garage 1358/21 20150129 4367.36 701100 20150228 BC R025 Ray 54977/10 20150202 4445.06 701108 20150303 H.M.V. K001 Jackie Tupp 917341 20150202 29976.77 701109 20150303 VMH H014 Honor Toze 659818 20150202 25022.46 701111 20150303 H.M.V. W007 Trevor Wills TJ9737 20150207 17674.1 701127 20150309 HMV T010 Wanda Farr 10028 A 20150207 48201.7 701129 20150309 BC M020 Luke Hair 2852 BNA 20150209 6125.21 701135 20150310 H.M.V. M128 Maurice Mynah IN-392835 20150212 9222.32 701143 20150313 HV B008 A Raid 3501057 20150219 93316.19 701152 20150320 WJT T006 Round Table BUR1351-G 20150209 48397.51 701156 20150310 WJT K001 Jackie Tupp 917346 20150216 40870.68 701158 20150317 VMH H014 Honor Toze 659823 20150216 14849.23 701160 20150317 HMV Z001 Wong Way 10002-97-213 20150223 83768.82 701169 20150324 V.S.T T010 Wanda Farr 10038 A 20150226 87175.59 701180 20150328 HMV R011 Polly Gunn L-1252/59 20150226 60841.77 701183 20150327 V.S.T M014 123478 20150303 97909.41 701196 20150402 HV F128 Ern Payed 82 98 1 20150306 75553.42 701200 20150403 HMV K001 Jackie Tupp 917347 20150306 81351.91 701210 20150404 CW W011 Witch Products 28G 20150310 6616.16 701219 20150409 HMV D025 Denise Bent 81346 20150317 35695.4 701227 20150416 VMH G010 Fixes 871491BUZ 20150317 13700.91 701233 20150416 VMH M025 Luke Hair 51555 20150326 62387.21 701251 20150424 WJT R007 Philip Upp Garage 1382/21 20150328 87051.45 701253 20150424 WJT T008 Truckstop 97 2053.29J 20150403 21544.61 701269 20150501 HMV T010 Wanda Farr 10050 A 20150330 55274.85 701278 20150428 WJT G010 Fixes 871495BUZ 20150330 4008.87 701279 20150428 VMH P009 P Green 566754T 20150406 44710.47 701282 20150505 BC M014 Linda Hand GR174 97 20150406 5710.81 701283 20150505 V.S.T P008 O Kay Yahs BC 469750 W 20150407 57416.87 701285 20150507 CB G010 Fixes 871502BUZ 20150410 11948.23 701309 20150509 VMH M025 Luke Hair 51574 20150417 94541.82 701322 20150516 V.S.T P006 N Rich 9724692 20150413 34068.89 701326 20150512 HV T004 Richmond IN/1157/97 20150421 524.93 701330 20150521 H.M.V. T006 Round Table BUR1358-G 20150421 16875.86 701331 20150521 H.M.V. D014 Chimps Teaparty 147894CTR 20150424 89224.57 701337 20150522 VMH K001 Joyce Tick 54700 20150424 39940.01 701338 20150522 WJT T009 Truckstop 97 2056.29J 20150424 3421.19 701340 20150523 HMV W020 Witch Products 35G 20150425 45682.68 701342 20150524 HMV W008 Winchester 1662 IOU-97 20150425 8808.62 701343 20150524 H.M.V. M025 Luke Hair 51589 20150420 64088.87 701344 20150519 WJT R008 Penny Cillin 100187 20150428 2662.37 701350 20150529 WJT M020 Linda Hand GR179 97 20150502 9402.53 701358 20150531 HV B008 A Raid 3501082 20150508 15888.34 701377 20150606 WJT T008 Tim Table 261221FF 20150511 7660.83 701389 20150609 WJT W020 Witch Products 36G 20150514 3629.61 701392 20150612 HMV W008 Winchester 1666 IOU-97 20150515 972.5 701393 20150612 H.M.V. Z001 Wong Way 10007-97-213 20150515 5487.14 701394 20150612 HMV W025 Wite Wash 2001/120.63 20150515 52278.64 701395 20150612 HMV F128 Edward Zoff 99807ABC 20150515 4527.58 701397 20150612 HV N001 Mike Atsil AZ364 20150712 18118.15 701398 20150810 CW H025 Ivan Aker WNZ61C 20150516 528.58 701399 20150615 HV B010 Carter Bout 971035A 20150511 36815.47 701401 20150609 HMV P009 P Green 566769T 20150521 7624.26 701409 20150619 WJT M014 Kurt N Upp 123502 20150525 13931.17 701421 20150623 CW R007 Philip Upp Garage 1400/21 20150529 17863.48 701426 20150626 WJT R025 Ray 54992/10 20150525 88643.66 701434 20150623 BC F123 Dick Tate 35256DUF 20150525 60709.45 701436 20150623 VMH H014 Honor Toze 659868 20150529 469.09 701437 20150627 WJT W020 Witch Products 39G 20150601 29691.39 701444 20150630 WJT W005 Will Scarlet 335/4/QPZ/1 20150604 21412.31 701448 20150703 WJT M020 Linda Hand GR195 97 20150604 2304.1 701454 20150703 WJT A128 A Meadow 117- 2349 20150605 25111.7 701460 20150704 CW G020 Gus Main T5774VB 20150605 47143.84 701462 20150704 WJT R008 Penny Cillin 100193 20150601 27421.39 701470 20150630 WJT F130 Farmer 14665 20150615 21436.8 701493 20150714 HMV R020 Polly Gunn L-1314/67 20150615 8662.03 701494 20150714 HMV F128 Ern Payed 82 141 1 20150622 1063.18 701511 20150721 HMV H014 IM Right 4888- 97 20150625 67522.83 701516 20150723 H.M.V. T006 Ronnie Biggs 7899477 20150625 13614.84 701517 20150724 H.M.V. W008 Willis S LMN/9.21 20150622 3890.76 701534 20150721 HMV P007 Nellie Dunn 000533CJW 20150629 1105.62 701536 20150728 VMH F130 Farmer 14670 20150630 65359.65 701539 20150730 HMV C202 Cary S Matic CS - 644 -97 20150630 6270.19 701546 20150730 CW A128 A Meadow 117- 2364 20150630 14723.28 701548 20150730 BC M014 Kurt N Upp 123511 20150630 24311.39 701553 20150730 HMV T005 Ri Pent PPN98874 20150630 32005.98 701559 20150730 HMV G020 Gus Main T5799VB 20150707 38292 701580 20150806 HMV M123 Luke Hair 51628 20150707 55600.88 701581 20150806 WJT R007 Philip Upp Garage 1434/21 20150809 4550.9 701583 20150807 HMV T005 Ri Pent PPN98878 20150809 31089.33 701584 20150807 WJT B008 A Raid 3501101 20150809 7948.57 701586 20150807 WJT T004 Richmond IN/1166/97 20150710 50437.78 701589 20150808 HMV T009 Truckstop 97 2066.29J 20150710 12011.47 701599 20150808 H.M.V. M123 Luke Hair 51631 20150710 90529.49 701603 20150808 V.S.T H025 Ivan Aker WNZ86C 20150713 1405.62 701606 20150811 BC R008 Penny Cillin 100223 20150713 5116.34 701609 20150811 BC P009 O Kay Yahs BC 469822 W 20150717 24225.07 701619 20150814 HV G020 Gus Main T5811VB 20150717 85695.08 701625 20150815 HMV T004 Ri Pent PPN98887 20150718 94185.24 701634 20150816 WJT H025 IM Right 4894- 97 20150720 17092.46 701637 20150817 HMV D025 Chimps Teaparty 147918CTR 20150724 27989.13 701646 20150821 H.M.V. W020 Witch Products 50G 20150724 20673.18 701651 20150822 H.M.V. W011 Winchester 1677 IOU-97 20150725 28231.17 701652 20150823 HMV B010 Carter Bout 971059A 20150727 16045.99 701660 20150824 HMV T010 Wanda Farr 10102 A 20150728 10534.22 701664 20150827 V.S.T M130 Microcomputers 5863MCC 20150730 7545.77 701672 20150828 VMH C202 Cary S Matic CS - 668 -97 20150731 3618.98 701673 20150828 BC P008 Noah Lott 9434 NL 20150731 74913.8 701679 20150829 HV R008 Philip Upp Garage 1441/21 20150727 64748.9 701685 20150825 WJT F123 Dick Tate 35277DUF 20150806 60468.26 701695 20150904 HMV R011 Pole Tacks LBQ-738-XY 20150807 25067.07 701697 20150904 WJT G010 Fixes 871544BUZ 20150814 28982.84 701717 20150911 BC M020 Linda Hand GR241 97 20150815 61565.46 701721 20150913 WJT T007 Round Table BUR1381-G 20150825 95767.75 701744 20150924 H.M.V. R011 Pole Tacks LBQ-747-XY 20150828 13098.33 701749 20150926 WJT Z001 Wong Way 10018-97-213 20150831 367.68 701757 20150928 HMV G010 Fixes 871549BUZ 20150904 46904.2 701763 20151002 HMV R020 Polly Gunn L-1347/73 20150904 79458.3 701765 20151002 WJT P010 P Green 566857T 20150904 27875.46 701766 20151002 WJT M020 Linda Hand GR244 97 20150904 39772.4 701767 20151002 V.S.T G020 Gus Main T5836VB 20150904 52350.68 701775 20151003 WJT M123 Luke Hair 51684 20150831 19563.4 701780 20150929 BC F130 Ern Payed 82 184 1 20150831 99670.53 701781 20150929 HMV T010 Wanda Farr 10120 A 20150908 23878.94 701791 20151008 HMV R020 Polly Gunn L-1353/74 20150911 66078.48 701794 20151009 HMV A128 A Meadow 117- 2395 20150907 70979.93 701802 20151006 BC M123 Luke Hair 51685 20150914 234.24 701806 20151013 HMV B008 A Raid 3501121 20150917 15045.62 701811 20151016 BC H025 IM Right 4911- 97 20150917 65695.51 701812 20151016 WJT D025 Chimps Teaparty 147935CTR 20150918 96206.56 701821 20151017 BC P009 O Kay Yahs BC 469870 W 20150925 77588.96 701844 20151024 CW A128 A Meadow 117- 2401 20150921 11015.78 701848 20151020 BC M123 Luke Hair 51708 20150921 24730.1 701856 20151020 WJT T005 Roger Peterson L-832648-1 20150928 35718.11 701859 20151027 WJT B008 A Raid 3501122 20150928 5691.62 701861 20151027 BC T006 Ronnie Biggs 7899506 20150928 95094.26 701863 20151027 BC H014 Honor Toze 659925 20150928 9951.94 701869 20151030 WJT T009 Truckstop 97 2080.29J 20151003 80648.49 701874 20151101 WJT W020 Witch Products 55G 20150928 34684.61 701876 20151027 HMV W005 Will Scarlet 350/4/QPZ/1 20151005 11694.27 701880 20151103 BC R008 Penny Cillin 100255 20151009 29686.09 701886 20151107 V.S.T W005 Wanda Farr 10134 A 20151010 81987.26 701889 20151108 HMV R020 Polly Gunn L-1363/76 20151005 2539.31 701892 20151103 HMV F130 1 Moore IN 6518 97 20151013 33680.54 701901 20151112 HMV P006 N Rich 9724815 20151017 10811.7 701912 20151116 CW H014 Honor Toze 659940 20151019 15707.88 701921 20151119 HMV T008 Tim Table 261261FF 20151020 12916.98 701925 20151119 V.S.T T009 Truckstop 97 2081.29J 20151020 95609.15 701926 20151119 V.S.T G010 Farmer 14703 20151024 38875.95 701935 20151122 BC P009 O Kay Yahs BC 469889 W 20151026 19856.98 701940 20151124 HMV W005 Wanda Farr 10137 A 20151101 73427.16 701957 20151130 HMV P010 P Green 566914T 20151102 96392.9 701961 20151201 HMV M020 Linda Hand GR278 97 20151102 75537.32 701962 20151201 HMV M025 Luke Hair 3010 BNA 20151102 55702.5 701963 20151201 H.M.V. P009 O Kay Yahs BC 469897 W 20151102 75192.58 701964 20151201 HMV N001 Microcomputers 5914MCC 20151102 21059.57 701965 20151201 HMV N005 Miles Long FR-1079 145 20151102 33332.69 701967 20151201 HMV B010 Carter Bout 971077A 20151103 70825.47 701970 20151203 H.M B010 Carter Bout 971084A 20151105 37760.87 701972 20151204 H.M.V. P007 Nellie Dunn 000570CJW 20151107 3508.84 701978 20151206 HMV G010 Farmer 14717 20151102 16631.14 701981 20151201 HMV N001 Mike Atsil AZ278 20150104 54721.86 701001 20150202 WJT B010 Carter Bout 971004A 20150104 6844.63 701004 20150203 CB P009 P Green 566698T 20150106 79237.49 701012 20150205 BC M020 Luke Hair 2828 BNA 20150106 1185.46 701014 20150205 WJT M025 L Hair 216 A2Z 20150112 24224.39 701025 20150212 HMV T006 Round Table BUR1345-G 20150112 94329.52 701036 20150212 HMV T008 Truckstop 97 2040.29J 20150112 54068.06 701045 20150212 BC W008 Winchester 1649 IOU-97 20150113 40542.22 701048 20150212 HMV W025 Wong Way 9999-97-213 20150114 59109.76 701049 20150215 WJT T009 Wanda Farr 10010 A 20150115 86441.66 701054 20150216 HMV M014 Linda Hand GR142 97 20150113 22398.62 701059 20150216 HMV M020 Luke Hair 2837 BNA 20150113 70509.14 701060 20150216 WJT Z001 Miles Long FR-969 39 20150119 795.59 701064 20150217 HMV D025 Denise Bent 81341 20150122 57998.57 701079 20150221 HV P010 Penny Cillin 100150 20150122 16700.56 701081 20150221 HMV T009 Wanda Farr 10020 A 20150119 47232.67 701084 20150223 HMV P008 O Kay Yahs BC 469708 W 20150126 39501.24 701091 20150226 HMV T005 Ri Pent PPN98787 20150129 45989.77 701101 20150228 V.S.T T006 Round Table BUR1350-G 20150202 12116.98 701107 20150303 WJT F123 Dick Tate 35220DUF 20150202 16155.1 701110 20150303 BC W008 Winchester 1651 IOU-97 20150205 77885.51 701119 20150306 HMV H025 Ivan Aker WNZ36C 20150205 47206.67 701123 20150306 VST B010 Carter Bout 971008A 20150205 38655.49 701125 20150307 VMH P006 Nellie Dunn 000509CJW 20150208 56863.54 701128 20150309 HMV C202 Cary S Matic CS - 581 -97 20150209 55923.17 701138 20150310 H.M.V. F130 1 Moore IN 6456 97 20150210 4267.33 701141 20150313 CW M014 Kurt N Upp 123468 20150212 62854.99 701145 20150313 HMV M025 Luke Hair 51532 20150212 9502.27 701148 20150314 HMV T005 Ri Pent PPN98804 20150115 20421.85 701150 20150315 H.M.V. N005 N Rich 9724646 20150209 657.43 701151 20150310 HV T004 Richmond IN/1148/97 20150209 51588.53 701155 20150310 WJT R010 Pole Tacks LBQ-690-XY 20150216 21738.15 701161 20150319 BC T007 Tim Table 261206FF 20150219 69895.8 701164 20150320 HMV W005 Will Scarlet 318/4/QPZ/1 20150223 98376.75 701171 20150324 H.M.V. H025 Ivan Aker WNZ39C 20150223 5118.2 701174 20150324 VST B010 Carter Bout 971016A 20150223 35309.02 701176 20150324 VMH G010 Fixes 871481BUZ 20150224 11744.73 701181 20150328 HV M130 Microcomputers 5767MCC 20150228 93585.39 701188 20150329 BC A128 A Meadow 117- 2309 20150226 7036.75 701191 20150328 H.V. P007 Noah Lott 9379 NL 20150301 32451.68 701195 20150402 HV M025 L Hair 248 A2Z 20150301 63440.86 701197 20150402 WJT T005 Ri Pent PPN98810 20150305 28785.09 701202 20150403 H.M.V. B008 A Raid 3501067 20150305 4404.87 701204 20150403 WJT H014 Honor Toze 659835 20150306 23250.89 701212 20150406 HMV T007 Tim Table 261208FF 20150309 39659.16 701216 20150409 BC W008 Winchester 1653 IOU-97 20150310 10025.39 701220 20150410 WJT F123 Edward Zoff 99804ABC 20150313 5768.49 701224 20150412 BC H025 Ivan Aker WNZ44C 20150315 1780.46 701226 20150414 VST R011 Polly Gunn L-1264/60 20150317 42789.73 701235 20150417 H.M.V. M020 Luke Hair 2864 BNA 20150319 55813.97 701238 20150417 HMV F130 1 Moore IN 6468 97 20150320 23071.46 701244 20150418 BC P007 Noah Lott 9381 NL 20150421 8990.82 701247 20150522 H.M.V. W025 Wite Wash 2001/116.63 20150402 38206.37 701274 20150501 BC F128 Edward Zoff 99805ABC 20150403 70403.18 701276 20150503 BC R011 Polly Gunn L-1269/61 20150406 53635.08 701281 20150505 H.M.V. M025 Luke Hair 51563 20150409 73516.66 701297 20150508 WJT F128 Ern Payed 82 113 1 20150409 28906.39 701298 20150508 VMH T005 Ri Pent PPN98831 20150409 79086.25 701300 20150508 BC P009 P Green 566760T 20150413 60242.14 701312 20150514 HMV P008 O Kay Yahs BC 469759 W 20150416 6711.33 701315 20150515 HV G020 Gus Main T5758VB 20150416 13012.52 701321 20150515 HMV H014 IM Right 4848- 97 20150419 34790.38 701328 20150519 VMH T005 Ronnie Biggs 7899464 20150419 16198.64 701329 20150519 WJT M025 Luke Hair 51590 20150424 43124.69 701345 20150523 WJT D025 Denise Bent 81351 20150427 11915.57 701348 20150529 HV G020 Gus Main T5765VB 20150505 6007.6 701366 20150608 WJT M128 Maurice Mynah IN-392869 20150507 41686.45 701367 20150605 CW P006 N Rich 9724706 20150507 1916.38 701376 20150606 CW T006 Round Table BUR1362-G 20150508 44905.96 701381 20150608 HMV W007 Trevor Wills TJ9753 20150517 28718.94 701403 20150616 H.M.V. C202 Cary S Matic CS - 620 -97 20150522 76220.09 701414 20150620 HMV Z001 Miles Long FR-1005 74 20150522 31038.95 701415 20150620 HV A128 A Meadow 117- 2343 20150522 50654.56 701416 20150622 CW G020 Gus Main T5768VB 20150524 40947.54 701418 20150623 WJT M123 Luke Hair 51602 20150526 5560.05 701424 20150626 BC H014 IM Right 4873- 97 20150529 21287.16 701430 20150629 CW D025 Chimps Teaparty 147903CTR 20150531 18583.59 701439 20150630 BC T008 Tim Table 261226FF 20150531 17889.1 701441 20150630 WJT P010 P Green 566787T 20150602 60469.35 701453 20150703 WJT M025 Luke Hair 2906 BNA 20150602 21678.05 701455 20150703 WJT P008 O Kay Yahs BC 469780 W 20150604 1424.29 701456 20150703 HV F130 Farmer 14658 20150609 24589.43 701475 20150710 H.M.V. M130 Microcomputers 5824MCC 20150609 21473.1 701481 20150710 VST Z001 Miles Long FR-1018 77 20150611 59333.5 701483 20150710 VST P007 Nellie Dunn 000526CJW 20150612 145.5 701490 20150712 CW G010 Fixes 871522BUZ 20150615 11410.16 701492 20150714 HMV P007 Noah Lott 9423 NL 20150619 49128.26 701506 20150720 VST R008 Phillippa Pail 97/1307/A 20150619 11741.07 701509 20150720 WJT H014 Honor Toze 659882 20150625 8076.69 701523 20150724 HMV R011 Pole Tacks LBQ-715-XY 20150626 14613.8 701524 20150725 WJT T010 Wanda Farr 10080 A 20150629 57754.32 701537 20150730 BC M130 Microcomputers 5842MCC 20150629 10927.45 701545 20150730 HMV M025 L Hair 297 A2Z 20150629 3656.26 701554 20150730 HMV N001 Mike Atsil AZ388 20150717 8717.65 701560 20150815 HMV R020 Polly Gunn L-1325/69 20150630 15185.05 701570 20150731 WJT F128 Ern Payed 82 154 1 20150706 6551.94 701582 20150806 HMV P006 N Rich 9724762 20150707 51690.07 701585 20150807 HMV K001 Jackie Tupp 917390 20150710 95890.94 701592 20150808 WJT M123 Luke Hair 51640 20150710 19639.94 701604 20150808 H.M.V. R020 Polly Gunn L-1328/70 20150714 44572.46 701615 20150814 WJT M130 Microcomputers 5852MCC 20150716 50776.75 701620 20150814 HV N005 Miles Long FR-1040 97 20150717 4089.67 701622 20150815 HV P008 Noah Lott 9425 NL 20150717 60194.66 701627 20150815 HMV B008 A Raid 3501107 20150717 15799.16 701636 20150816 CB T006 Ronnie Biggs 7899489 20150720 973.76 701638 20150818 HMV R025 Ray 54999/10 20150720 73953.63 701641 20150820 WJT K001 Jackie Tupp 917400 20150721 38727.8 701642 20150821 WJT Z001 Wong Way 10012-97-213 20150724 81318.72 701653 20150823 WJT P007 Nellie Dunn 000547CJW 20150727 93461.99 701663 20150825 HV P010 P Green 566833T 20150727 74654.01 701668 20150827 HMV N005 Miles Long FR-1046 106 20150730 22949.53 701674 20150828 VMH M014 Kurt N Upp 123521 20150731 72093.97 701680 20150829 HMV M025 L Hair 317 A2Z 20150731 47901.59 701681 20150830 WJT R010 Phillippa Pail 97/1334/A 20150731 76891.73 701682 20150830 HMV T004 Ri Pent PPN98890 20150730 33830.46 701686 20150828 V.S.T P006 N Rich 9724788 20150803 3965.3 701687 20150901 HV T006 Ronnie Biggs 7899494 20150803 124.15 701690 20150901 BC B010 Carter Bout 971060A 20150811 7447.07 701712 20150911 HMV T010 Wanda Farr 10110 A 20150812 8865.03 701716 20150911 BC G010 Farmer 14689 20150812 14600.93 701718 20150911 WJT M128 Maurice Mynah IN-392920 20150818 75031.08 701730 20150918 VMH P008 Noah Lott 9441 NL 20150818 47377.86 701731 20150918 HV M025 L Hair 330 A2Z 20150820 3316.16 701733 20150918 HMV F130 Ern Payed 82 174 1 20150821 22480.76 701736 20150919 WJT K001 Jackie Tupp 917413 20150825 7882.88 701746 20150925 BC T008 Tim Table 261245FF 20150828 9551.01 701752 20150927 BC W005 Will & Wont 18/JMB/0729 20150929 72972.54 701754 20151030 V.S.T W020 Witch Products 52G 20150831 49503.02 701755 20150928 HMV N005 Miles Long FR-1051 118 20150904 73925.39 701772 20151003 VMH M128 Maurice Mynah IN-392928 20150904 57857.56 701776 20151005 VMH R010 Phillippa Pail 97/1353/A 20150830 36130.16 701779 20150929 WJT Z001 Mike Atsil AZ408 20150907 24728.37 701784 20151006 VMH R008 Penny Cillin 100241 20150907 30607.37 701788 20151008 WJT P007 Nellie Dunn 000557CJW 20150907 12219.44 701790 20151008 VMH M020 Linda Hand GR256 97 20150911 23011.03 701796 20151010 WJT M130 Microcomputers 5882MCC 20150911 21555.49 701799 20151012 VMH G020 Gus Main T5844VB 20150913 27388.73 701804 20151013 HMV T004 Ri Pent PPN98921 20150914 6150.08 701809 20151015 HMV R007 Peter Rabbitt KG0385 20150918 79901.01 701820 20151017 BC Z001 Mike Atsil AZ409 20150717 77086.87 701830 20150816 VMH R008 Penny Cillin 100244 20150921 28048.91 701834 20151022 WJT W008 Trevor Wills TJ9776 20150921 49375.29 701835 20151022 HV R020 Polly Gunn L-1359/75 20150922 91715.65 701840 20151023 BC D014 Cary S Matic CS - 690 -97 20150925 17024.1 701846 20151026 BC N005 Miles Long FR-1059 128 20150925 73089.25 701847 20151026 CW T005 Richmond IN/1182/97 20150927 49215.5 701864 20151027 WJT T007 Round Table BUR1389-G 20150928 52179.96 701865 20151029 BC W025 Wite Wash 2001/142.63 20150927 2955.65 701879 20151027 HMV M025 L Hair 358 A2Z 20151016 23150.18 701906 20151114 HMV H025 IM Right 4929- 97 20151017 48063.97 701914 20151116 HMV D025 Chimps Teaparty 147939CTR 20151018 29415.5 701923 20151119 HV M025 Joyce Tick 54824 20151018 81544.17 701924 20151119 HMV W020 Witch Products 56G 20151020 23175.56 701928 20151120 WJT F128 Edward Zoff 99842ABC 20151022 40135.53 701933 20151120 HMV P010 P Green 566899T 20151023 76353.12 701937 20151122 H.M.V. M020 Linda Hand GR269 97 20151023 24854.73 701938 20151122 BC M025 Luke Hair 3002 BNA 20151018 4781.47 701939 20151119 H.M.V. D014 Cary S Matic CS - 705 -97 20151026 70907.38 701942 20151126 VMH N005 Miles Long FR-1073 140 20151027 7962.64 701943 20151127 HMV H025 Ivan Aker WNZ105C 20151026 47197.22 701951 20151124 HMV W008 Trevor Wills TJ9785 20151026 4582.11 701955 20151124 HMV G020 Fixes 871562BUZ 20151101 90287.15 701958 20151201 HMV K001 Ivan Aker WNZ106C 20151102 48885.06 701968 20151203 HV W008 Trevor Wills TJ9787 20151103 3561.25 701974 20151204 HMV R020 Polly Gunn L-1368/79 20151108 19620.22 701982 20151208 WJT N005 Miles Long FR-1082 155 20151113 8795.55 701989 20151212 HMV B008 A Meadow 117- 2413 20151113 19816.88 701990 20151213 H.V. N005 Miles Long FR-1090 163 20151116 1731.52 702001 20151215 WJT F123 Dick Tate 35239DUF 20150522 78105.1 701334 20150622 VMH Z001 Wong Way 10006-97-213 20150501 57980.56 701273 20150602 H.M.V. M025 L Hair 269 A2Z 20150508 36539.36 701295 20150609 WJT W005 Wanda Farr 10140 A 20151206 66244.39 701979 20151206 WJT B008 A Raid 3501068 20150425 76113.99 701256 20150531 WJT H014 Gus Main T5866VB 20151127 15913.11 701946 20151128 VMH W005 Will Scarlet 316/4/QPZ/1 20150115 85728.78 701051 20150215 H.M.V. M128 Maurice Mynah IN-392838 20150331 85074.72 701194 20150508 H.M.V. F130 1 Moore IN 6469 97 20150507 2844.68 701290 20150613 PJM F130 Farmer 14627 20150327 13802.27 701182 20150328 WJT R025 Ray 55006/10 20151016 512.77 701816 20151124 WJT
IDEA Data Analysis Workbook/Source Files.ILB/Customer.txt
Bright IDEAs Inc Customer Master Listing _______________________________________________________________________________________________________ Account Number Name Address Credit Limit ______________ __________________________ ____________________ ____________ A001 Dan Ackroyd Audenshaw 20000 125 New Street Montreal Quebec H2S 3H2 A123 Mike Atsil The Vetinary House 20000 123 Dog Row Thunder Bay Ontario K3A 7G1 A128 Ivan Aker The Old House 10000 Ottawa Ontario P1D 8D4 B001 Kim Basinger Mesh House 12000 Fish Street Rouyn Quebec J5V 2A9 B002 Richard Burton Eagle Castle 9000 Leafy Lane Sudbury Ontario L3M 9S3 Account Number Name Address Credit Limit B004 Jeff Bridges Arrow Road North 20000 Lakeside Kenora Ontario N3R 2S1 B008 Denise Bent The Dance Studio 20000 Covent Garden Montreal Quebec H5S 3H2 B010 Carter Bout Removals Close 20000 No Fixed Abode Road Toronto Ontario M2A 7D3 B022 Ronnie Biggs Gotaway Cottage 5000 Thunder Bay Ontario K3A 6F3 C001 Tom Cruise The Firm 25000 Gunnersbury Waskaganish Quebec G1A 6H5 Account Number Name Address Credit Limit C003 John Candy The Sweet Shop 15000 High Street Trois Rivieres Quebec J6V 3A1 C004 Jamie Lee Curtis Wanda Avenue 25000 Welham Green Montreal Quebec H4S 3H2 C005 Kevin Costner Water Way 50000 Wet Street Ottawa Ontario P1D 8D4 C008 Wyn Chester Gun Lane 35000 Sudbury Ontario L3M 9S8 C020 Penny Cillin The Doctors Surgery 15000 Green Park Chibougamau Quebec G1A 1G3 Account Number Name Address Credit Limit D002 Robert De Niro The Ring 10000 Madison Square Garden Orleans Ontario P4A 6D8 D003 Sammy Davis Jnr MGM Grand 30000 Rouyn Quebec J5V 2A7 D014 William Ditt Builders, 2 The Lane 25000 Lower Themdown Kenora Ontario N3R 2S7 D018 Danny De Vito Come Quick Taxis 20000 Waskaganish Quebec G1A 6H4 D025 Nellie Dunn Comeback Tommorrow 15000 Neverland Thunder Bay Ontario K3A 7G5 Account Number Name Address Credit Limit F001 Jodie Foster The Water Mill 25000 Jack Hill Trois Rivieres Quebec J6V 2A9 F002 Morgan Freeman Bonehurst Road 18000 Redhill Toronto Ontario M1A 7D9 F003 Douglas Fairbanks J Universal Studios 20000 Chicoutimi Quebec G2A 1A3 F010 Polly Filla 10 The Gap 10000 Bath Sudbury Ontario L3M 9S7 F053 Harrison Ford Rue de Saint Michel 25000 Ottowa Ontario P1D D8 Account Number Name Address Credit Limit F128 M Akem Fixit Plumbers House 10000 Mendham Trois Rivieres Quebec J6V 2A8 F130 Daisy Farmer The Old Farmhouse 10000 Lower Field Chibougamau Quebec G4A 3A3 G001 Clark Gable Tara 20000 Kenora Ontario N3R 2S5 G010 Bill Gates Microcomputers plc 15000 Technology Park Ottawa Ontario P1D 8D7 G020 P Green Painters Lane 20000 11 Wall Street Rouyn Quebec J5V 2A7 Account Number Name Address Credit Limit H002 Bob Hopeful Faraway Court 35000 Singapore Road Waskaganish Quebec G1A 6H3 H003 Tom Hanks Sea of Tranquility 10000 Moon Street Trois Rivieres Quebec J6V 2A6 H014 Hanson Brothers The Rink 20000 Ice Hockey Street Thunder Bay Ontario K3A 7G4 H025 Luke Hair Plum in the Mouth Close 20000 The Old Manor Toronto Ontario M1A 7D8 K002 Nicole Kidman Heathfield 8000 Hampstead Orleans Ontario P4A 6D7 Account Number Name Address Credit Limit L020 Miles Long Kangeroo Park 20000 Thunder Bay Ontario K3A 7G6 M001 Robert Mitchum The Bull Ring 15000 Chibougamau Quebec G6A 1G3 M002 Dean Martin Any Bar 25000 Any Place Ottawa Ontario P1D 8D5 M003 Bill Murray Sheet Street 20000 Ghost Town Sudbury Ontario L3M 9S6 M004 Lee Marvin No Name Court 25000 Wandering Star Road Rouyn Quebec J5V 2A6 Account Number Name Address Credit Limit M014 Cary S Matic The Style Council 25000 Style House Orleans Ontario P4A 6D6 M025 A Meadow Field Lane 20000 Little Village Chibougamau Quebec G4A 1G3 M123 1 Moore Strangeways 20000 Chicoutimi Quebec G2A 3G3 M128 Gus Main The Pipeworks 10000 Oilcombe Montreal Quebec H2S 3H7 M130 Maurice Mynah Beaulieu Car Museum 25000 Beaulieu Kenora Ontario N3R 2S4 Account Number Name Address Credit Limit N003 Paul Newman Charlestown Chiefs 20000 Charlestown Ottawa Ontario P1D 8D3 N005 K Non Church Street 5000 Westminster Orleans Ontario P4A 6D5 O007 James Bond The Hilton Hotel Beach Avenue Trois Rivieres Quebec J6V 2A4 P001 Michelle Pfeiffer The Tower 10000 BatWoman Close Orleans Ontario P4A 6D4 P002 Richard Pryor Colthrop Way 30000 Victoria Toronto Ontario M1A 7D6 Account Number Name Address Credit Limit P004 Jason Patric TCR 30000 Montreal Quebec H2S 3H6 P005 Al Pacino Rue de la Reine 25000 Avenue Charles de Gaulle Chibougamau Quebec G2A 1H3 P006 Phillippa Pail The Old Dairy 5000 Mosley Street Chicoutimi Quebec G2A 1G3 P007 Mandy Pumps The Fire Brigade Building 10000 Water Close Kenora Ontario N3R 2S3 P008 Ern Payed Bill Factors Lane 10000 Carnival Street Rouyn Quebec J5V 2A5 Account Number Name Address Credit Limit R001 Julia Roberts Broad Street Mall 8000 Hearst Ontario P3A 6D3 R002 Tim Robbins Player Close 10000 Durham Road Toronto Ontario M1A 7D2 R003 Rene Russo The Galleries 20000 Broadmead Trois Rivieres Quebec J6V 2A1 R005 N Rich The Organic Fertiliser Co 25000 Compost Lane Sudbury Ontario L3M 9S4 R007 A Raid Castle Street 5000 Montreal Quebec H2S 3H5 Account Number Name Address Credit Limit R010 Winona Ryder Roman Way 25000 Coleshill Orleans Ontario P4A 6D3 R011 John Richmond St Georges Walk 10000 Chibougamau Quebec G2A 4G3 R020 A Round The Circle 15000 Stonehenge Waskaganish Quebec G1A 6H2 R025 G.I. Rate Spinning Road 20000 Highgate Kenora Ontario N3R 2S2 S001 Frank Sinatra Broadway 20000 Toronto Ontario M1A 7D1 Account Number Name Address Credit Limit S003 Sylvester Stallone Rocky Road 6000 The Ring Thunder Bay Ontario K3A 7G3 S004 Charlie Sheen Cromwell Road 25000 Bayswater Ottawa Ontario P1D 8D2 S010 Truck Stop Ltd The Pop In Cafe 15000 Barley Mow Passage Trois Rivieres Quebec J6V 2A2 S025 O J Simpson The Court House 35000 Glove Lane Chibougamau Quebec G2A 7G7 T001 John Travolta Main Street 20000 Richmond Rouyn Quebec J5V 2A4 Account Number Name Address Credit Limit T003 Jackie Tupp The Garage 10000 Chokingham Toronto Ontario M1A 7D3 T004 Dick Tate Secretarial Services Ltd 20000 Type Road Chicoutimi Quebec G2A 7G4 T005 Honor Toze The Ballet School 5000 Sundance Village Sudbury Ontario L3M 9S2 T006 Pole Tacks 2 Much Street 20000 Timmins Ontario P2A 6D1 T007 Chimps Teaparty House of Commons 10000 Westminster Toronto Ontario M1A 7D5 Account Number Name Address Credit Limit T009 Tim Table Railways Board 5000 Orleans Ontario P4A 6D3 T010 Andrew Truffles Split Ends 35000 Coles Forest Orleans Ontario P4A 6D1 T011 Elizabeth Taylor Piggott Way 16000 Chicoutimi Quebec G2A 7G5 U009 Phil M Upp The Garage 25000 Chokingham Rouyn Quebec J5V 2A2 V001 Darth Vader 1 The Dark Side 20000 Hearst Ontario P3A 6D2 Account Number Name Address Credit Limit W002 Denzil Washington Ricochet Tower 75000 Crimson Street Montreal Quebec H2S 3H3 W004 Sky Ward Hot Spots Travel 13000 The Promenade Sudbury Ontario L3M 9S1 W006 John Wayne Green Fields Stables 5000 Horseferry Road Chicoutimi Quebec G2A 7G4 W009 James Woods Digstown 10000 Oliver County Waskaganish Quebec G1A 6H1 W010 Robin Williams The BirdCage 10000 Rouyn Quebec J5V 2A1 Account Number Name Address Credit Limit W025 World Wide Wait Cobweb Park 20000 Http Way Toronto Ontario M1A 7D4 Z001 Z Inc Wall Street 20000 Montreal Quebec H2S 3H1
IDEA Data Analysis Workbook/Source Files.ILB/Inventory 2015.asc
110211059 QUEBEC 32 4.368 139.776 100 25 18 760948 20151204 9.15 20150513 8.45 535 4.02 111201103 OTTAWA 125 3.511 438.875 300 100 70 760474 20151108 8.75 20150812 7.95 794 3.694 111601902 QUEBEC 670 0.69 462.3 900 200 540 760848 20151203 1.49 20151202 1.35 2488 0.638 111807059 QUEBEC 684 0.591 404.244 750 100 432 760949 20151204 1.09 20150413 0.99 2156 0.55 111807558 QUEBEC 61 1.877 114.497 300 100 48 760950 20151204 3.95 20101105 3.55 86 1.732 111813056 TORONTO 990 0.548 542.52 3000 500 900 760951 20151204 1.15 20151006 1.05 493 0.504 111815558 QUEBEC 140 0.571 79.94 500 100 150 760516 20151114 1.19 20150319 1.05 101 0.525 111821608 OTTAWA 360 0.482 173.52 388 169 400 760849 20151203 1.35 20150430 1.29 1011 0.493 111821703 QUEBEC 380 0.401 152.38 404 127 200 760851 20151203 1.15 20150606 1.09 3756 0.4 111821809 TORONTO 250 0.46 115 250 100 150 760852 20151203 1.29 20150221 1.19 100 0.474 111824705 QUEBEC 320 0.652 208.64 431 166 200 760853 20151203 1.75 20150528 1.59 1635 0.649 113219101 QUEBEC 9 3.299 29.691 Y 7 4 12 760386 20151018 7.59 20101016 7.25 189 3.102 113401101 TORONTO 63 0.85 53.55 125 63 60 760952 20151204 2.09 20150701 2.29 590 0.81 113401704 OTTAWA 60 0.97 58.2 50 10 45 760953 20151204 2.35 20150526 2.15 49 0.92 113411019 QUEBEC 20 2.83 56.6 66 0 20 760462 20151108 3.45 20151108 0 0 0 116003054 TORONTO 240 0.505 121.2 217 59 240 760418 20151029 1.05 20151112 0.95 778 0.458 116003551 OTTAWA 360 0.899 323.64 288 119 1152 760334 20150925 1.9 20150213 1.75 488 0.833 116030109 QUEBEC 4 5.19 20.76 Y 103 2 20 760427 20151031 12.65 20150810 10.95 85 4.995 116030904 OTTAWA 35 2.245 78.575 Y 103 2 100 760045 20150212 5.5 20150914 4.99 242 2.17 116035403 QUEBEC 70 0.949 66.43 81 0 50 760954 20151204 2.29 20150726 2.09 79 0.91 117202655 QUEBEC 900 0.35 315 870 335 450 760796 20151202 0.99 20101006 0 2676 0.319 117208754 QUEBEC 300 0.403 120.9 265 58 300 760798 20151202 1.15 20150523 0.99 386 0.378 117231008 QUEBEC 100 0.978 97.8 130 40 100 760854 20151203 2.19 20150818 2.15 0 0.87 120250100 QUEBEC 315 3.014 949.41 327 164 360 760509 20151113 7.49 20150602 7.25 1362 3.101 120254902 QUEBEC 60 5.32 319.2 173 62 50 760855 20151203 11.75 20150109 10.95 55 4.892 123202607 QUEBEC 133 0.683 90.839 231 91 160 760609 20151121 1.75 20101002 1.69 0 0 123418809 QUEBEC 80 0.976 78.08 164 82 120 760721 20151129 2.45 20151020 2.25 427 0.859 133006658 QUEBEC 82 5.402 442.964 216 8 60 760955 20151204 11.35 20150218 9.85 0 4.928 133007087 TORONTO 59 7.155 422.145 172 61 36 760956 20151204 14.95 20150612 13.95 10 6.548 135215507 QUEBEC 108 1.79 193.32 111 56 108 760684 20151128 4.59 20150204 4.39 0 0 135240058 QUEBEC 39 1.953 76.167 56 10 18 760957 20151204 4.29 20150204 4.25 23 1.889 135277655 OTTAWA 140 0.505 70.7 137 19 48 760958 20151204 1.09 20101110 1.05 740 0.485 135277951 OTTAWA 126 1.934 243.684 301 150 120 760566 20151118 4.45 20150723 0 108 1.934 135278356 TORONTO 60 1.449 86.94 223 37 72 760517 20151114 3.15 20150725 0 534 1.384 135278557 OTTAWA 45 2.172 97.74 186 68 30 760959 20151204 4.75 20150222 0 348 2.065 135278959 QUEBEC 114 1.01 115.14 191 96 72 760960 20151204 2.25 20150426 0 50 0.99 135279353 TORONTO 210 2.12 445.2 168 59 162 760567 20151118 4.59 20150714 4.29 1176 2.02 135279755 QUEBEC 172 1.581 271.932 188 69 96 760961 20151204 3.59 20150819 3.29 58 1.581 142000202 QUEBEC 280 0.83 232.4 374 162 250 760856 20151203 1.49 20150716 1.29 1657 0.77 142017756 QUEBEC 186 2.065 384.09 349 74 60 760962 20151204 4.29 20150829 3.75 0 1.889 142018055 QUEBEC 222 2.328 516.816 378 89 120 760568 20151118 4.85 20101015 4.39 1481 2.131 142019052 TORONTO 13 2.812 36.556 60 0 30 760672 20151126 5.75 20150906 5.35 380 2.545 142019158 OTTAWA 40 3.384 135.36 107 0 60 760569 20151118 6.95 20150116 6.45 11 3.07 143000501 QUEBEC 0 1.76 0 200 50 50 760551 20151117 4.75 20150711 4.55 543 1.76 143001001 OTTAWA 255 2.663 679.065 429 140 200 760857 20151203 5.45 20150415 4.99 1375 1.79 143031214 OTTAWA 246 3.54 870.84 222 36 162 760204 20150630 4.56 20151111 4.33 130 3.21 143600404 QUEBEC 50 3.385 169.25 90 20 45 760858 20151203 8.55 20150103 7.85 28 3.162 143611055 QUEBEC 75 11.906 892.95 60 0 35 760570 20151118 21.45 20150927 0 479 11.044 150204805 QUEBEC 150 1.785 267.75 270 85 30 704450 20080925 4.09 20101201 3.79 751 1.6 150206503 QUEBEC 12 5.816 69.792 210 105 20 760859 20151203 13.25 20101124 11.95 642 5.528 160410509 QUEBEC 410 1.983 813.03 378 139 480 760550 20151116 5.35 20101103 4.99 4488 2.19 164011152 QUEBEC 230 0.712 163.76 234 92 360 760685 20151128 1.99 20101226 2.19 0 0.79 164011302 QUEBEC 320 0.729 233.28 406 153 160 760860 20151203 2.15 20150417 1.99 1078 0.69 164011408 QUEBEC 162 1.89 306.18 155 52 72 760861 20151203 3.99 20151225 4.45 617 1.889 164011609 OTTAWA 280 0.463 129.64 449 200 170 760862 20151203 1.35 20151010 1.42 1450 0.496 164011905 OTTAWA 100 2.19 219 230 65 70 760552 20151117 5.95 20100927 5.45 910 2.189 220205305 QUEBEC 0 67.139 0 Y 200 100 24 760096 20150330 99.95 20150625 0 339 62.26 220601502 QUEBEC 59 30.691 1810.769 247 99 60 760146 20150427 47.95 20150103 45.95 676 29.87 220604504 TORONTO 0 43.55 0 Y 175 88 18 760136 20150422 69.95 20150527 59.95 265 43.55 222070007 QUEBEC 5 21.287 106.435 104 52 18 760380 20151015 46.25 20151223 43.85 250 21.287 232217705 QUEBEC 105 1.221 128.205 209 55 60 761013 20151205 3.45 20150504 0 46 1.2 232217809 QUEBEC 100 1.268 126.8 300 0 30 761014 20151205 3.59 20150629 0 32 1.25 232219308 QUEBEC 70 0.94 65.8 231 41 90 760518 20151114 2.39 20150920 0 2890 0.94 232220307 QUEBEC 200 0.247 49.4 235 18 200 761015 20151205 0.69 20150817 0 287 0.24 232825608 QUEBEC 190 0.267 50.73 152 1 250 760510 20151113 0.69 20151217 0.65 122 0.264 232826806 QUEBEC 110 0.47 51.7 138 69 90 760863 20151203 1.39 20151228 1.29 113 0.47 232830902 QUEBEC 70 0.92 64.4 81 16 50 760864 20151203 2.85 20151224 2.59 1277 0.92 233214209 TORONTO 100 0.725 72.5 130 40 100 760388 20151018 2.15 20150607 1.95 212 0.713 233260592 QUEBEC 20 0.909 18.18 300 0 20 760429 20151102 2.69 20150128 2.35 590 0.902 233302301 OTTAWA 160 0.47 75.2 500 10 90 761080 20151205 1.69 20150521 1.49 407 0.47 233709604 QUEBEC 40 0.755 30.2 32 0 35 761081 20151205 2.25 20101116 2.05 12 0.754 233805409 QUEBEC 20 4.92 98.4 166 58 9 761082 20151205 10.15 20150328 10.95 221 4.915 260200409 QUEBEC 72 20.735 1492.92 158 4 60 760463 20151108 28.95 20100823 30.95 2148 20.823 260800109 QUEBEC 11 3.58 39.38 234 42 5 760464 20151108 7.75 20150815 7.25 111 3.578 311110078 QUEBEC 710 3.149 2235.79 618 0 800 761083 20151205 4.95 20151123 5.39 1730 3.142 311304572 TORONTO 185 0.729 134.865 223 112 80 760865 20151203 1.49 20150315 1.35 2145 0.65 311705575 QUEBEC 170 1.87 317.9 186 68 100 760868 20151203 3.49 20150501 3.19 2177 1.74 312509507 QUEBEC 46 7.486 344.356 212 56 72 760871 20151203 13.29 20100823 12.25 651 7.08 312707209 TORONTO 264 7.278 1921.392 386 193 210 760459 20151108 12.99 20150517 12.49 659 6.9 312708904 TORONTO 36 7.281 262.116 29 0 72 760371 20151010 14.99 20151130 13.99 2058 7.018 312904076 OTTAWA 56 12.878 721.168 195 47 40 760874 20151203 21.99 20150612 19.99 424 11.749 313149407 TORONTO 80 5.814 465.12 214 7 360 760305 20150914 13.95 20150827 0 789 5.95 313149519 TORONTO 241 4.65 1120.65 218 34 420 704485 20081010 6.75 20151128 6.52 180 4.22 313303800 OTTAWA 1 16.821 16.821 201 100 4 704478 20080929 29.95 20150625 0 811 17.19 313304003 QUEBEC 0 16.822 0 Y 150 0 5 700731 20010921 29.95 20151130 0 406 17.19 313305201 QUEBEC 0 21.89 0 225 88 6 704672 20081118 44.95 20151106 0 58 21.89 313305603 TORONTO 7 35.089 245.623 56 28 2 700731 20010921 59.95 20150304 0 164 35.19 313314312 OTTAWA 230 5.6 1288 409 205 960 760302 20150912 7.33 20151128 6.98 860 5.2 313333209 QUEBEC 10 1.27 12.7 183 92 1200 760603 20151121 2.95 20150923 0 1918 1.33 313401400 QUEBEC 0 63 -67 207 79 12 760579 20151119 120 20150620 119.99 238 62.308 313402005 QUEBEC 3 62 186 127 0 4 760580 20151119 120 20151029 109.95 0 0 316002505 QUEBEC 170 1.704 289.68 Y 286 143 160 760480 20151109 3.39 20101115 2.95 3278 1.689 316003502 OTTAWA 51 5.534 282.234 Y 66 0 30 760493 20151113 11.15 20101101 9.99 188 5.242 316004203 TORONTO 130 0.53 68.9 Y 229 40 180 760604 20151121 1.29 20150331 1.25 290 0.53 319000202 QUEBEC 535 2.01 1075.35 603 302 300 760877 20151203 3.79 20101230 3.49 2905 1.97 319001200 OTTAWA 280 1.83 512.4 424 212 30 761145 20050128 3.75 20101110 0 6685 1.83 319502103 OTTAWA 150 3.36 504 120 10 100 760481 20151109 7.25 20150118 6.49 1833 3.24 319502801 QUEBEC 390 0.619 241.41 537 244 400 760494 20151113 1.29 20100905 1.19 621 0.58 321701072 QUEBEC 340 0.869 295.46 272 86 800 760419 20151029 1.95 20151125 1.79 1637 0.869 330701905 OTTAWA 56 18.239 1021.384 95 22 20 760880 20151203 35.99 20101108 0 379 18.05 330720907 QUEBEC 48 17.507 840.336 88 19 72 760252 20150731 32.99 20150501 0 162 18.48 330721109 OTTAWA 28 22.709 635.852 1000 0 36 760271 20150816 39.99 20150503 0 111 23 340115109 QUEBEC 1290 0.952 1228.08 1182 491 640 760382 20151015 1.79 20151031 0 10895 0.94 340130008 OTTAWA 1340 1.019 1365.46 1222 561 1440 760460 20151108 2.55 20151029 2.35 549 0.871 340307008 QUEBEC 550 0.995 547.25 640 245 960 760310 20150915 2.35 20151215 2.15 8012 0 340308519 QUEBEC 60 1.12 67.2 173 87 160 760390 20151019 1.86 20150713 1.95 168 1.32 340720655 QUEBEC 282 3.05 860.1 351 125 72 760689 20151128 6.99 20150326 0 878 3.05 350156702 OTTAWA 780 0.24 187.2 749 300 480 760800 20151202 0.59 20150928 0.55 260 0.24 350157001 TORONTO 168 1.58 265.44 334 117 78 760802 20151202 3.69 20101111 3.75 4016 1.58 350157403 TORONTO 558 1.708 953.064 596 248 600 760673 20151126 4.99 20150113 5.25 4364 1.89 350160407 QUEBEC 880 4.345 3823.6 729 265 500 760804 20151202 9.49 20101130 8.39 1378 4.173 350161002 QUEBEC 87 4.55 395.85 220 85 40 760806 20151202 9.99 20150215 8.69 1360 4.347 350190305 QUEBEC 197 4.95 975.15 233 66 108 760883 20151203 10.99 20151212 0 1004 4.95 380101905 QUEBEC 61 7.766 473.726 174 87 60 760610 20151121 20.75 20150223 18.99 93 7.643 380102003 QUEBEC 205 4.57 936.85 214 32 100 760886 20151203 13.39 20150115 12.49 110 4.648 380301707 QUEBEC 130 3.698 480.74 279 115 120 760889 20151203 9.29 20150416 8.69 1594 3.631 380500702 QUEBEC 1130 0.863 975.19 1079 490 400 760892 20151203 2.39 20151002 2.19 6261 0.863 380700409 OTTAWA 3940 1.686 6642.84 3152 1476 1875 760511 20151113 2.65 20150904 2.95 22692 1.687 380700809 QUEBEC 1775 0.352 624.8 1545 773 550 760553 20151117 0.79 20150924 0.75 3960 0.352 381100509 OTTAWA 615 1.347 828.405 667 234 600 760895 20151203 2.95 20101027 2.75 7680 1.342 381100700 OTTAWA 930 0.661 614.73 769 310 480 760623 20151121 1.39 20150819 1.29 9527 0.633 381502107 OTTAWA 101 7.164 723.564 181 15 80 760554 20151117 17.99 20150301 17.49 442 7.171 381700401 TORONTO 75 3.309 248.175 285 143 100 760896 20151203 9.49 20151227 8.49 335 3.309 382101001 QUEBEC 188 11.461 2154.668 Y 300 150 300 760897 20151203 27.95 20150726 26.95 645 12.24 382102506 QUEBEC 6 1.64 9.84 Y 105 52 140 760438 20151104 3.89 20101216 3.55 1016 1.676 410513006 TORONTO 18 16.969 305.442 189 95 24 760414 20151028 39.49 20101211 0 610 17 420304105 TORONTO 104 3.279 341.016 133 17 120 760690 20151128 7.99 20150801 0 1677 3.15 420304401 OTTAWA 140 3.12 436.8 287 69 96 760963 20151204 7.99 20101020 0 1089 3 420317818 QUEBEC 86 9.71 835.06 94 22 160 760420 20151029 10.48 20101022 9.6 160 9.32 420317913 OTTAWA 74 8.32 615.68 59 0 140 760421 20151029 12.6 20150924 10.9 280 7.8 421020509 QUEBEC 25 22.05 551.25 45 23 6 760738 20151130 54.95 20150517 46.95 40 22.049 421028604 TORONTO 20 5.109 102.18 141 71 20 760964 20151204 19.95 20150118 13.95 228 5.109 421033602 QUEBEC 14 16.3 228.2 161 0 24 760465 20151108 34.95 20150415 32.95 366 16.299 421036403 QUEBEC 0 56.5 0 225 13 12 760344 20150929 119 20150319 105 45 0 421036805 QUEBEC 0 13.75 0 125 63 24 760355 20151003 34.95 20150705 29.95 16 13.749 421103002 QUEBEC 0 25.4 0 25 0 22 760353 20151003 64.99 20101107 59.99 148 27.558 421106004 TORONTO 23 16.202 372.646 93 0 22 760374 20151011 42.99 20151023 39.99 292 18.472 421114205 QUEBEC 5 34.15 170.75 29 0 12 760356 20151003 80.49 20151004 74.95 25 34.025 421131308 QUEBEC 3 32.76 98.28 152 76 18 760335 20150927 75.95 20150929 69.95 116 31.657 421143307 TORONTO 1 24.102 24.102 Y 10 0 12 760327 20150919 0 20150924 0 18 28.098 421152008 TORONTO 0 13.86 0 200 100 22 760346 20150930 32.99 20101130 29.99 56 14.556 421201505 QUEBEC 0 14.577 0 150 75 18 760314 20150917 36.95 20150423 32.95 102 14.324 421202909 OTTAWA 42 7.024 295.008 84 17 24 760739 20151130 18.95 20101009 17.95 7 7.024 421208904 QUEBEC 0 6.6 50 0 0 30 760324 20150919 16.95 20151218 15.95 248 6.584 421290103 QUEBEC 0 19.05 0 25 0 12 760315 20150917 56.95 20151121 49.95 58 18.428 421290706 QUEBEC 0 7.35 0 225 88 24 760254 20150803 18.95 20150326 16.95 125 7.347 421300405 OTTAWA 0 9.371 0 Y 125 38 24 760295 20150911 22.95 20101214 0.2 0 0 421511008 QUEBEC 88 2.57 226.16 70 10 48 760965 20151204 5.25 20101205 6.99 0 0 421531007 OTTAWA 16 3.2 51.2 13 0 256 760406 20151025 7.49 20150329 0 0 0 421613009 TORONTO 0 11.27 0 125 0 11 760365 20151008 25.99 20150225 0 155 11.27 421621009 QUEBEC 0 22.93 0 175 5 8 760366 20151008 52.99 20151012 0 25 22.93 430110101 QUEBEC 201 3.807 765.207 236 68 216 760740 20151130 7.99 20101009 0 7 3.64 430125102 QUEBEC 24 4.84 116.16 Y 169 85 310 704561 20081113 11.99 20101204 0 204 4.84 430128200 TORONTO 41 19.7 807.7 183 16 90 760519 20151114 43.95 20100825 39.95 57 19.699 430161006 TORONTO 60 8.59 515.4 273 112 80 760605 20151121 19.99 20150827 0 625 8.59 430301115 OTTAWA 30 6.338 190.14 174 12 48 761016 20151205 13.49 20151027 0 173 5.89 430315005 TORONTO 51 12.968 661.368 266 58 48 761017 20151205 28.49 20150823 0 248 12.39 430351004 TORONTO 111 6.769 751.359 289 94 36 761018 20151205 18.49 20150608 0 822 7.087 430501118 OTTAWA 132 8.412 1110.384 106 3 60 761019 20151205 18.99 20101004 0 1287 8.09 430562409 QUEBEC 120 9.82 1178.4 125 11 54 760966 20151204 12.6 20150201 11.9 420 9.76 430902504 OTTAWA 12 3.78 45.36 35 0 36 760741 20151130 8.95 20150429 8.75 3387 3.779 430903205 QUEBEC 4 3.124 12.496 28 0 54 760630 20151122 6.99 20151214 0 1276 3 430903406 TORONTO 145 2.987 433.115 241 20 30 760638 20151123 6.49 20150818 5.45 605 2.85 430906006 OTTAWA 72 3.945 284.04 258 29 72 761020 20151205 8.95 20151018 0 559 3.78 430910007 OTTAWA 120 3.967 476.04 321 61 72 761021 20151205 11.95 20150824 10.99 119 4.969 440100608 QUEBEC 88 5.08 447.04 120 35 64 761022 20151205 10.99 20150918 9.95 410 5.08 440105028 TORONTO 37 8.38 310.06 255 127 30 760674 20151126 19.95 20150106 17.95 0 0 440106017 OTTAWA 32 10.43 333.76 151 0 10 760967 20151204 21.95 20150406 19.95 0 0 440120406 TORONTO 0 12.816 0 200 75 72 760094 20150316 27.49 20151104 24.95 1369 12.706 440120808 QUEBEC 0 13.156 0 175 63 72 760154 20150507 27.49 20150515 24.95 1171 12.965 440124606 QUEBEC 0 9.483 0 0 0 70 760084 20150306 17.95 20150315 16.75 548 8.8 440129105 QUEBEC 2 19.9 39.8 152 1 15 760132 20150421 39.95 20100823 0 42 19.9 440303000 QUEBEC 96 9.387 901.152 252 101 48 760898 20151203 18.99 20150420 17.95 140 9.387 440330308 OTTAWA 310 14.976 4642.56 398 149 140 760520 20151114 29.99 20151222 27.99 1073 13.773 440400808 OTTAWA 84 13.6 1142.4 267 134 96 760624 20151121 29.95 20150515 26.95 58 13.598 440402601 OTTAWA 86 19.7 1694.2 94 0 50 760691 20151128 25 20150327 22.5 186 17.68 440404003 QUEBEC 24 36 864 69 0 24 760370 20151010 39.5 20150422 38 49 36 460112202 QUEBEC 940 0.731 687.14 827 339 320 760899 20151203 1.79 20150728 1.89 12655 0.73 460121009 QUEBEC 540 0.582 314.28 482 241 1440 760512 20151113 1.19 20150704 1.09 21456 0.582 460151002 QUEBEC 280 0.729 204.12 Y 249 100 960 760692 20151128 1.59 20101010 1.49 31109 0.744 460181006 TORONTO 400 0.665 266 545 198 400 760900 20151203 1.39 20150710 1.29 4309 0.666 460183508 OTTAWA 1940 0.779 1511.26 1702 776 640 760693 20151128 1.39 20150709 1.29 21158 0.795 460186404 OTTAWA 1390 0.663 921.57 1312 556 1280 760901 20151203 1.79 20150313 1.49 2595 0.728 460310105 OTTAWA 220 1.759 386.98 176 38 80 760902 20151203 4.29 20101003 4.99 2789 1.861 460310602 QUEBEC 370 1.7 629 496 248 320 760903 20151203 4.29 20150730 4.99 5337 1.867 460907100 OTTAWA 425 1.128 479.4 390 195 300 760555 20151117 2.79 20100930 2.59 1376 1.127 460908001 QUEBEC 200 1.194 238.8 285 68 750 760430 20151102 2.89 20151021 2.69 2958 1.194 460910008 QUEBEC 6 5.444 32.664 55 0 48 760606 20151121 12.49 20101021 0 8290 5.45 460914007 OTTAWA 240 6.609 1586.16 267 59 240 761023 20151205 16.99 20100826 15.99 244 6.42 460931004 QUEBEC 150 6.77 1015.5 295 123 96 760968 20151204 13.99 20100904 15.75 1742 6.84 460935003 OTTAWA 30 6.088 182.64 24 12 60 760631 20151122 11.99 20150528 13.75 1170 5.93 460970608 OTTAWA 184 2.215 407.56 272 136 128 760513 20151113 5.69 20151215 0 2184 2.215 461104205 QUEBEC 330 0.976 322.08 489 245 240 760904 20151203 1.99 20150911 1.89 2082 0.974 461107207 OTTAWA 720 1.028 740.16 776 313 240 760905 20151203 2.19 20101101 1.79 19344 1.022 461109709 OTTAWA 680 0.326 221.68 719 335 640 760694 20151128 0.85 20151230 0 8556 0.325 461109804 QUEBEC 660 0.379 250.14 728 364 320 760906 20151203 0.89 20150821 0 1326 0.313 461110000 QUEBEC 750 2.132 1599 750 300 140 760907 20151203 3.99 20150723 0 868 2.4 461115006 QUEBEC 190 1.391 264.29 177 39 1200 760322 20150918 2.39 20150918 2.99 10538 1.391 461116212 OTTAWA 410 0.45 184.5 378 139 210 760908 20151203 0.7 20150720 0.67 1206 0.412 462501900 QUEBEC 1116 0.76 848.16 1043 446 864 760695 20151128 1.39 20151230 1.29 2051 0.759 462518207 OTTAWA 875 0.45 393.75 875 338 450 760909 20151203 0.99 20150326 0.95 3895 0.45 510503002 OTTAWA -8 9.081 0 112 56 42 760812 20151202 23.95 20151227 21.95 62 8.75 510509207 QUEBEC 250 1.774 443.5 200 50 150 761024 20151205 4.95 20150310 4.5 1004 1.774 510536505 QUEBEC 120 8.418 1010.16 221 86 135 760675 20151126 19.95 20150918 17.99 410 7.81 510539000 QUEBEC 165 2.17 358.05 307 129 200 760676 20151126 5.5 20150731 0 3979 2.17 510541503 TORONTO 90 5.87 528.3 297 149 80 760813 20151202 14.9 20150201 0 7169 5.87 521102302 QUEBEC 14 7.2 100.8 36 0 10 760654 20151124 15.95 20101226 15.5 328 7.222 521342007 QUEBEC 7 28.79 201.53 Y 206 78 40 760345 20150929 33.95 20150609 66 17 29.6 521363003 QUEBEC 20 42.29 845.8 Y 191 21 15 760126 20150417 49.95 20150430 95 155 42.726 521365609 QUEBEC 7 33.8 236.6 Y 31 15 2 760276 20150826 39.95 20151113 85 59 34.542 530543706 OTTAWA 32 7.7 246.4 251 125 20 760696 20151128 15.5 20150311 14.95 115 7 530543907 QUEBEC 40 11.55 462 207 104 30 760697 20151128 29.95 20101227 0 17 10.5 530545309 QUEBEC 71 31.26 2219.46 107 0 48 860473 20150420 54.95 20101119 34.95 870 28.42 530656009 QUEBEC 20 19.5 390 41 0 24 760441 20151104 23.7 20101022 22.7 84 18.6 530754605 QUEBEC 6 16.55 99.3 Y 5 0 26 760256 20150807 34.95 20150317 0 165 16.55 530915012 QUEBEC 30 5.18 155.4 Y 249 75 216 760318 20150917 14.95 20150109 14.25 262 5.18 530942006 TORONTO 198 5.12 1013.76 358 179 162 761025 20151205 12.95 20150401 11.45 1303 5.072 530942408 OTTAWA 60 5.115 306.9 98 24 54 761026 20151205 12.95 20150911 11.45 303 4.996 530942609 TORONTO 78 5.119 399.282 262 56 54 760762 20151202 12.95 20150630 11.45 759 5.01 530944009 TORONTO 249 3.58 891.42 199 100 108 761027 20151205 9.95 20101007 8.95 2422 3.584 531180001 QUEBEC 4 6.9 27.6 Y 103 27 28 760422 20151029 15.95 20150216 0 1689 6.9 531210503 QUEBEC 154 3.58 551.32 248 74 80 761028 20151205 8.95 20150627 7.95 305 3.448 531220307 OTTAWA 177 3.09 546.93 192 21 96 761029 20151205 7.95 20150521 6.95 98 3 531220402 QUEBEC 93 3.09 287.37 124 62 96 760523 20151114 7.95 20151021 6.95 239 3 531220603 QUEBEC 108 2.16 233.28 186 43 96 760524 20151114 5.5 20151017 4.95 389 2.089 531230005 OTTAWA 96 2.16 207.36 127 13 96 760742 20151130 5.5 20151116 4.95 19 2.043 531360206 TORONTO 99 11.92 1180.08 304 102 60 760253 20150731 23.95 20150214 21.95 206 11.92 531609952 OTTAWA 37 9.54 352.98 30 0 40 760814 20151202 21.95 20150122 19.95 105 9.367 531620006 TORONTO 4 10.38 41.52 178 64 32 760347 20150930 19.95 20150417 0 85 10.005 531901003 OTTAWA 60 4.36 261.6 48 0 20 760815 20151202 10.95 20151223 9.95 402 4.161 531901701 QUEBEC 90 2.15 193.5 147 0 80 760677 20151126 5.5 20151005 4.95 223 1.845 640701500 QUEBEC 0 0.768 0 150 50 1110 704262 20080513 1.79 20150423 1.59 3707 0.729 640712004 OTTAWA 0 1.901 0 0 0 170 704361 20080805 3.45 20150227 2.99 1649 1.689 640714506 QUEBEC 230 0.72 165.6 384 192 25 760348 20150930 1.69 20150527 1.35 757 0.63 640720501 TORONTO 0 2.62 0 100 0 340 704362 20080805 5.95 20150222 4.95 341 2.484 640807500 TORONTO 0 0.88 0 0 0 410 704364 20080807 1.69 20101024 1.59 205 0.83 640814005 QUEBEC 0 1.06 0 200 0 110 704365 20080826 2.95 20150719 2.69 310 1.012 640818004 TORONTO 0 1.94 0 150 0 40 704366 20080901 4.75 20150803 5.49 199 1.83 640819509 TORONTO 0 1.155 0 150 0 385 704368 20080901 2.85 20151103 3.25 1167 1.11 641107009 TORONTO 33 18.015 594.495 76 13 12 760495 20151113 27.49 20150429 24.7 92 16.71 641108008 OTTAWA 41 25.338 1038.858 183 41 20 760415 20151028 42.99 20150823 37.95 107 23.52 641108505 QUEBEC 21 28.976 608.496 167 58 10 761030 20151205 42.5 20150426 38.25 161 26.81 641119000 OTTAWA 29 32.932 955.028 123 0 10 760655 20151124 49.95 20151215 44.95 52 30.749 641121006 OTTAWA 4 50.734 202.936 178 64 2 760491 20151111 75.95 20150603 68.95 1 46.96 641123508 QUEBEC 24 30.222 725.328 69 0 6 761031 20151205 44.5 20150206 39.99 127 27.848 641126002 QUEBEC 4 62.751 251.004 153 2 6 760328 20150921 99.95 20150606 94.5 30 59.428 641400707 QUEBEC 54 16.11 869.94 168 59 10 760496 20151113 28.95 20101019 0 1067 16.11 641405005 TORONTO 24 4.833 115.992 219 60 8 760442 20151106 11.95 20151003 10.95 72 4.6 642400403 OTTAWA 0 1.097 0 125 0 768 760385 20151017 2.49 20101017 2.75 680 1.02 642402503 TORONTO 180 1.462 263.16 144 72 480 760394 20151022 2.75 20150604 2.45 3373 1.364 642405008 QUEBEC 30 3.57 107.1 99 0 72 760325 20150919 6.95 20150524 7.95 555 3.571 643001818 OTTAWA 240 0.868 208.32 242 21 70 761032 20151205 1.99 20151122 1.89 828 0.81 643003812 QUEBEC 2250 0.129 290.25 1875 888 1500 760601 20151120 0.49 20101114 0.39 468 0.125 643035506 QUEBEC 330 3.369 1111.77 414 182 300 760257 20150807 4.57 20150924 4.25 529 3.373 643037787 QUEBEC 1720 2.226 3828.72 1601 751 9000 760443 20151106 3.49 20100920 3.29 53080 2.296 643037882 OTTAWA 860 4.733 4070.38 813 407 1500 760454 20151106 7.29 20101210 6.99 2071 4.83 660202502 OTTAWA 125 1.438 179.75 225 113 30 760352 20151001 3.49 20100915 3.39 220 1.374 660208009 OTTAWA 240 0.568 136.32 367 109 100 760816 20151202 1.79 20151207 1.69 190 0.549 660211002 QUEBEC 120 1.277 153.24 121 61 60 760817 20151202 3.99 20150905 3.49 221 1.229 660218003 QUEBEC 85 1.512 128.52 268 109 25 760251 20150731 3.69 20150524 3.25 32 1.464 660218509 OTTAWA 88 0.662 58.256 120 10 80 760818 20151202 1.89 20150617 1.69 106 0.647 660221007 TORONTO 76 0.804 61.104 186 0 80 760819 20151202 1.99 20151103 1.69 148 0.775 660221504 QUEBEC 104 2.533 263.432 258 104 80 760820 20151202 6.59 20150413 5.99 366 2.448 660222702 QUEBEC 105 0.663 69.615 259 55 60 760821 20151202 1.89 20150214 1.65 394 0.646 660302001 QUEBEC 55 2.466 135.63 44 0 50 760822 20151202 6.59 20150823 5.95 302 2.38 660402008 OTTAWA 70 1.723 120.61 256 78 25 760823 20151202 4.59 20100826 0 714 1.657 660408001 QUEBEC 90 0.361 32.49 247 24 40 760825 20151202 0.99 20150419 0.89 922 0.347 660414504 QUEBEC 240 0.328 78.72 292 146 200 760827 20151202 0.99 20150226 0.89 2108 0.289 660900109 OTTAWA 0 0.479 0 150 50 550 760185 20150621 1.99 20101129 0 5775 0.444 660900257 OTTAWA 0 0.36 0 25 0 150 760187 20150621 1.09 20101003 0 212 0.342 660901350 QUEBEC 0 0.49 0 175 0 140 760189 20150621 1.79 20100922 1.59 12071 0.48 660901559 QUEBEC 0 1.11 0 200 0 65 760175 20150606 3.79 20150325 0 2071 1.109 660901952 TORONTO 0 1.32 0 100 25 55 760175 20150606 3.69 20101125 0 6473 1.327 660902018 OTTAWA 430 1 430 519 160 600 760296 20150911 1.7 20150607 1.5 790 0.98 660902113 OTTAWA 620 0.12 74.4 671 336 600 760475 20151108 0.22 20100909 0.21 2300 0.11 660902558 OTTAWA 0 0.32 0 75 0 130 760155 20150516 1.29 20151009 0.99 1290 0.308 661205602 QUEBEC 192 2.29 439.68 354 102 150 860483 20150520 4.99 20150927 4.49 101 2.18 661226503 TORONTO 80 1.5 120 64 7 80 760602 20151120 4.19 20150302 3.95 101 1.481 661242006 QUEBEC 2 14.932 29.864 27 0 16 760052 20150215 29.95 20151119 0 4 14.938 661250006 OTTAWA 150 0.608 91.2 170 85 50 760497 20151113 1.65 20150319 1.49 946 0.55 661256507 TORONTO 624 2.89 1803.36 699 350 840 860484 20150520 6.45 20150930 5.99 2347 2.748 830103504 OTTAWA 105 0.54 56.7 184 42 30 760910 20151203 1.89 20151008 1.79 1373 0.54 830105001 OTTAWA 50 1.577 78.85 40 20 220 760423 20151029 3.49 20150317 2.59 890 1.438 830106009 QUEBEC 10 0.225 2.25 183 0 200 760424 20151029 0.75 20150422 0.69 1734 0.206 830107503 OTTAWA 44 2.57 113.08 260 105 64 760698 20151128 6.95 20150424 5.95 285 2.541 830110507 QUEBEC 105 0.395 41.475 309 105 60 760911 20151203 2.49 20151013 2.45 2838 0.734 830120501 OTTAWA 64 2.244 143.616 201 1 48 760699 20151128 6.95 20150908 5.95 7 2.058 850604509 OTTAWA 36 6.46 232.56 79 0 30 760743 20151130 14.29 20150831 13.49 543 5.375 850622008 OTTAWA 84 1.843 154.812 117 9 72 760744 20151130 4.99 20151209 4.49 3922 1.85 850660909 QUEBEC 84 1.801 151.284 267 59 108 760571 20151118 3.99 20150811 0 0 1.82 851401509 OTTAWA 344 2.315 796.36 375 163 252 760912 20151203 4.99 20150414 3.99 5262 2.242 851520000 OTTAWA 6 0.85 5.1 80 40 144 760372 20151010 2.29 20100918 1.99 3151 0.849 911747707 OTTAWA 190 1.524 289.56 227 64 230 760913 20151203 4.49 20101027 3.99 1481 1.524 911748006 OTTAWA 125 2.526 315.75 250 100 115 760914 20151203 6.49 20150309 5.79 2448 2.524 911749300 TORONTO 43 10.02 430.86 259 55 56 760764 20151202 25.99 20101005 0 234 10.02 930103103 QUEBEC 112 3.52 394.24 90 0 72 761033 20151205 7.09 20151225 6.49 17 3.52 1041023009 OTTAWA 192 1.708 327.936 254 52 120 761034 20151205 3.99 20150723 3.59 312 1.558 1041024209 OTTAWA 186 1.708 317.688 199 49 126 761035 20151205 3.99 20151025 3.59 709 1.547 1041027209 QUEBEC 90 1.87 168.3 297 124 126 760411 20151026 4.29 20151027 3.99 728 1.603 1041032304 QUEBEC 78 1.699 132.522 287 69 240 760525 20151114 3.99 20151010 3.59 2757 1.528 1041710009 OTTAWA 68 6.927 471.036 54 0 48 760745 20151130 13.99 20100830 11.99 405 5.888 1050100102 OTTAWA 156 1.64 255.84 350 175 216 760556 20151117 4.99 20101227 0 233 1.49 1050153509 OTTAWA 0 2.407 0 100 50 96 760482 20151109 6.29 20150430 5.69 38 2.194 1050153700 QUEBEC 39 4.996 194.844 31 0 42 761036 20151205 12.65 20101210 11.49 778 4.545 1050155006 TORONTO 372 0.97 360.84 498 249 456 760431 20151102 2.45 20151215 2.19 9292 0.879 1050157009 TORONTO 342 1.1 376.2 449 0 372 760592 20151119 3.29 20150928 2.69 3027 1.1 1050190706 QUEBEC 576 1.36 783.36 561 280 288 760969 20151204 3.99 20151017 3.59 485 1.24 1050194409 QUEBEC 66 3.144 207.504 128 64 96 760593 20151119 7.55 20150804 6.99 191 3.084 1050516654 QUEBEC 24 2.283 54.792 244 22 132 760432 20151102 5.59 20151019 4.85 3022 2.04 1050525801 QUEBEC 18 2.283 41.094 189 70 132 760476 20151108 5.59 20151020 4.85 4053 2.04 1050528209 OTTAWA 294 2.284 671.496 310 55 264 760746 20151130 5.59 20150902 4.85 2618 2.139 1050595306 QUEBEC 150 1.256 188.4 195 98 372 760433 20151102 3.35 20150222 2.75 1122 1.236 1050596409 OTTAWA 1272 2.98 3790.56 1218 509 1152 760748 20151130 6.99 20150904 5.75 613 2.816 1050711208 OTTAWA 246 2.507 616.722 347 98 360 760286 20150906 6.39 20100826 5.95 3764 2.36 1050947504 OTTAWA 114 2.766 315.324 216 83 162 760288 20150906 7.29 20150829 6.89 1435 2.516 1060717552 TORONTO 180 2.237 402.66 244 47 144 760625 20151121 5.99 20150204 5.15 2010 2.034 1060717569 QUEBEC 78 4.368 340.704 137 0 90 760915 20151203 10.99 20150522 9.99 835 4.138 1060903300 QUEBEC 300 1.068 320.4 340 170 168 761037 20151205 2.59 20101111 2.15 412 0.915 1061501207 OTTAWA 672 0.261 175.392 663 306 720 760557 20151117 0.99 20101211 1.19 19078 0.31 1070102200 TORONTO 672 1.24 833.28 538 169 432 761084 20151206 3.45 20150313 3.19 587 1.16 1070124500 OTTAWA 1332 0.47 626.04 1291 570 1296 761085 20151206 1.09 20100824 0.95 1203 0.44 1070152509 QUEBEC 96 5.569 534.624 252 126 192 760282 20150828 12.75 20101226 12.49 336 5.149 1070162006 QUEBEC 1494 1.76 2629.44 1195 548 1536 760970 20151204 4.45 20150412 4.25 579 1.695 1070163003 QUEBEC 222 2.259 501.498 328 64 108 760971 20151204 5.99 20150228 5.75 1420 2.165 1070172009 QUEBEC 624 0.66 411.84 400 50 648 760972 20151204 2.39 20100909 2.15 1485 0.66 1070172900 QUEBEC 84 1.37 115.08 192 21 60 760632 20151122 3.29 20100913 2.99 713 1.258 1070202502 TORONTO 1608 2.705 4349.64 1286 593 648 760594 20151119 6.15 20150224 6.49 3143 2.447 1070205303 OTTAWA 780 1.199 935.22 674 287 672 761086 20151206 3.09 20101025 2.79 7056 1.098 1070319208 OTTAWA 280 1.847 517.16 299 75 240 760973 20151204 3.25 20150213 2.99 1149 1.668 1070319303 OTTAWA 10 3.76 37.6 83 0 200 760595 20151119 7.99 20150116 7.15 122 3.651 1070401508 QUEBEC 144 2.91 419.04 215 83 180 760466 20151108 7.69 20151006 6.95 277 2.66 1070402008 TORONTO 84 3.417 287.028 142 0 108 760750 20151130 8.69 20101204 7.99 1023 3.21 1070406007 QUEBEC 24 2.14 51.36 219 0 2160 760467 20151108 4.59 20150607 4.99 15629 1.986 1070501007 OTTAWA 228 0.64 145.92 257 104 108 760752 20151130 1.69 20150611 1.49 1896 0.59 1070515508 OTTAWA 204 0.58 118.32 363 132 216 760974 20151204 1.35 20150316 1.19 8077 0.58 1070541005 QUEBEC 360 3.874 1394.64 288 44 240 761087 20151206 7.69 20100916 6.99 1052 3.61 1070588100 TORONTO 402 1.34 538.68 400 60 780 760633 20151122 3.25 20150128 2.89 4744 1.328 1070588406 OTTAWA 660 0.31 204.6 553 277 864 760428 20151101 0.95 20150430 0.85 1589 0.29 1070588501 TORONTO 150 1.01 151.5 270 135 144 760634 20151122 2.99 20150828 2.69 1639 1.008 1070602009 OTTAWA 36 0.91 32.76 80 40 204 760514 20151113 2.05 20151210 1.85 2606 0.896 1070801207 LONDON 900 0.94 846 400 200 500 761088 20151206 3.15 20151001 2.85 800 0.882 1070801503 OTTAWA 336 2.42 813.12 294 147 576 760635 20151122 6.25 20150708 5.89 1261 2.355 1110301509 QUEBEC 616 0.61 375.76 518 234 672 760829 20151202 1.95 20100926 0 0 0.777 1110701009 OTTAWA 636 0.622 395.592 684 242 348 760975 20151204 1.99 20151104 1.79 0 0.613 1110701506 QUEBEC 372 0.861 320.292 448 199 168 760976 20151204 2.79 20100908 2.59 0 0.846 1110906008 QUEBEC 216 1.019 220.104 373 161 168 760916 20151203 4.49 20151017 3.95 2445 0.945 1110906505 QUEBEC 264 1.412 372.768 211 56 372 760515 20151113 3.49 20100925 2.89 15376 1.389 1111104502 QUEBEC 0 1.119 0 175 63 576 760164 20150528 2.69 20151019 2.49 8075 1.055 1112110003 TORONTO 96 1.07 102.72 102 0 128 760977 20151204 4.49 20151114 4.19 0 1.06 1112110701 OTTAWA 46 3.21 147.66 262 31 36 760978 20151204 9.69 20150322 8.99 0 3.18 1112111009 TORONTO 114 3.281 374.034 91 46 132 760979 20151204 10.99 20150205 10.15 0 3.25 1112122500 TORONTO 360 0.48 172.8 388 144 360 761038 20151205 1.29 20151104 0.99 796 0.45 1112123507 QUEBEC 150 1.125 168.75 120 0 156 761039 20151205 3.19 20150704 2.99 917 1.099 1112138009 OTTAWA 360 1.536 552.96 338 144 288 760917 20151203 4.19 20151002 3.79 5149 1.388 1112320009 TORONTO 186 2.668 496.248 299 99 240 761040 20151205 6.49 20100828 5.49 2611 2.398 1112503650 QUEBEC 1776 0.491 872.016 1446 698 816 760626 20151121 1.29 20101220 0.99 14737 0.483 1112503859 TORONTO 696 0.904 629.184 782 291 480 760918 20151203 2.49 20150603 1.95 14596 0.823 1112520902 TORONTO 360 2.96 1065.6 313 57 576 760477 20151108 7.49 20151015 0 721 2.96 1112522304 QUEBEC 570 0.481 274.17 581 191 480 761041 20151205 1.35 20101111 1.25 675 0.46 1112522801 QUEBEC 348 1.05 365.4 303 152 576 760643 20151124 2.69 20150718 2.59 1272 0.995 1120101501 TORONTO 1300 1.545 2008.5 1090 445 780 760754 20151130 3.49 20100905 2.79 704 1.364 1120303001 OTTAWA 610 0.623 380.03 663 282 700 760980 20151204 1.89 20150805 1.35 1134 0.584 1120304009 QUEBEC 330 0.624 205.92 389 145 300 760981 20151204 1.89 20150513 1.75 1758 0.593 1120314003 QUEBEC 42 2.03 85.26 184 17 120 760596 20151119 4.99 20150305 4.35 693 2.007 1130104602 QUEBEC 204 0.76 155.04 313 157 480 760392 20151019 1.99 20151022 1.89 1657 0.72 1130302505 OTTAWA 102 0.92 93.84 207 103 336 760404 20151025 2.25 20101226 0 1394 0.92 1130310609 OTTAWA 360 0.92 331.2 313 132 144 760700 20151128 2.25 20150812 0 186 0.92 1130311703 TORONTO 156 0.92 143.52 175 0 144 760701 20151128 2.25 20150710 0 2573 0.92 1130312709 OTTAWA 222 1.7 377.4 203 26 180 760919 20151203 4.49 20101231 4.15 2240 1.7 1130312806 OTTAWA 426 1.7 724.2 566 233 504 760920 20151203 4.49 20151014 4.15 6780 1.7 1130352009 OTTAWA 126 1.86 234.36 176 0 120 760702 20151128 4.19 20100907 3.69 5490 1.688 1130360201 OTTAWA 192 1.688 324.096 279 114 288 760439 20151104 4.19 20151002 3.69 6952 1.688 1130370809 QUEBEC 120 1.7 204 121 0 252 760558 20151117 4.49 20151007 0 0 1.7 1130370904 OTTAWA 108 1.7 183.6 286 43 84 760921 20151203 4.49 20150803 0 0 1.7 1130371002 QUEBEC 420 1.7 714 511 156 384 760440 20151104 4.49 20150628 0 0 1.7 1130371108 QUEBEC 324 1.7 550.8 484 192 384 760478 20151108 4.49 20150429 0 0 1.7 1130371309 QUEBEC 180 2.386 429.48 144 22 192 760703 20151128 5.49 20150121 0 9689 2.27 1130371500 OTTAWA 264 2.294 605.616 261 81 192 760922 20151203 5.49 20151112 0 8820 2.27 1130371709 OTTAWA 72 2.252 162.144 133 66 384 760283 20150830 5.49 20150726 0 9348 2.27 1130701006 OTTAWA 924 0.46 425.04 939 420 360 860313 20150311 1.65 20150301 1.39 1822 0.46 1130702109 QUEBEC 132 0.841 111.012 331 115 384 760405 20151025 1.89 20150629 1.75 27129 0.76 1130704008 OTTAWA 576 0.48 276.48 586 293 336 860316 20150311 1.19 20150414 0.99 5562 0.47 1131111206 QUEBEC 580 6.537 3791.46 589 270 624 760319 20150917 16.49 20101224 14.99 150 6.677 1312112000 OTTAWA 951 74.191 70555.641 986 443 189 760400 20151025 99.95 20100907 0 3928 72.92 1313120500 OTTAWA 1723 33.937 58473.451 1378 639 540 760401 20151025 49.95 20100828 0 1786 36.44 1318201508 OTTAWA 1370 5.19 7110.3 1246 598 30 760022 20081230 9.95 20150506 0 7461 5.19 1318211407 QUEBEC 780 1.312 1023.36 674 337 880 760042 20150129 2.49 20151201 2.99 5029 1.472 1319019405 OTTAWA 440 0.72 316.8 452 226 900 760036 20150120 1.59 20100828 0 740 0.721 1319019606 QUEBEC 460 0.72 331.2 543 197 600 760038 20150120 1.59 20151227 0 2767 0.722 1320102882 QUEBEC 202 10.329 2086.458 337 68 84 760035 20150111 17.49 20150226 16.95 1907 10.43 1320150103 OTTAWA 2182 17.026 37150.732 1871 835 2196 760402 20151025 24.95 20150716 27.95 8256 17.932 1410103609 QUEBEC 145 0.47 68.15 141 21 75 760982 20151204 0.99 20151230 0 2659 0.451 1412009508 TORONTO 204 1.922 392.088 213 7 168 760425 20151029 4.49 20151123 3.99 1038 1.8 1415102501 QUEBEC 35 10.783 377.405 178 0 120 760456 20151107 21.99 20150521 19.95 984 10.23 1415104305 QUEBEC 27 10.334 279.018 197 48 54 760284 20150901 21.99 20100921 0 665 9.85 1415106003 TORONTO 18 10.283 185.094 39 20 33 760367 20151008 21.99 20150103 0 202 9.85 1415392209 QUEBEC 130 4.614 599.82 304 152 310 760457 20151107 9.49 20150103 8.95 119 4.327 1415398700 OTTAWA 210 1.908 400.68 268 134 320 760458 20151107 3.99 20150809 0 500 1.76 1415400104 TORONTO 65 7 455 77 0 450 760407 20151025 16.99 20101208 15.95 1691 7 1415801001 TORONTO 210 2.075 435.75 318 84 108 760627 20151121 4.99 20150108 3.99 121 1.996 1416401506 OTTAWA 27 16.158 436.266 97 23 14 760983 20151204 37.99 20150526 34.95 33 14.893 1421104001 OTTAWA 560 0.402 225.12 448 224 960 760678 20151126 1.05 20150205 0.95 4720 0.383 1421122302 OTTAWA 240 0.157 37.68 242 121 360 760572 20151118 0.32 20150813 0.35 262 0.13 1421123606 QUEBEC 96 0.59 56.64 Y 202 76 144 760074 20150302 1.35 20150226 1.19 298 0.483 1421123902 TORONTO 270 0.54 145.8 416 183 240 760984 20151204 1.35 20150428 1.19 350 0.54 1421123953 QUEBEC 198 0.99 196.02 283 92 360 760408 20151025 2.45 20101016 2.15 1706 0.99 1421124307 QUEBEC 288 0.99 285.12 330 140 144 760985 20151204 2.45 20150828 2.15 2110 0.99 1421201006 TORONTO 640 0.111 71.04 662 306 300 760831 20151202 0.3 20100908 0.35 2637 0.106 1421201503 QUEBEC 620 0.196 121.52 646 248 800 760679 20151126 0.52 20150314 0.65 708 0.185 1421221407 QUEBEC 492 0.045 22.14 Y 494 247 540 760244 20150727 0.17 20100904 0.14 1114 0.045 1421221809 OTTAWA 624 0.109 68.016 699 250 360 760986 20151204 0.22 20101109 0.19 1031 0.092 1421223209 OTTAWA 282 0.348 98.136 351 75 150 760987 20151204 0.79 20150513 0.75 1942 0.309 1421223507 OTTAWA 366 0.583 213.378 343 171 288 760468 20151108 1.29 20151015 1.15 1863 0.52 1422101500 TORONTO 1540 0.775 1193.5 1307 579 1280 760095 20150321 1.99 20151127 1.69 7199 0.757 1423112007 QUEBEC 684 0.337 230.508 547 199 216 760274 20150822 0.99 20150607 0.59 4257 0.259 1430205503 QUEBEC 250 11.13 2782.5 250 75 260 760214 20150713 21.99 20101028 20.99 780 10.296 1430406006 TORONTO 93 10.88 1011.84 74 0 144 760273 20150821 19.99 20101230 17.99 59 9.812 1430500305 TORONTO 197 4.76 937.72 358 179 180 760293 20150908 9.95 20150202 8.95 452 4.394 1433212705 QUEBEC 28 13.31 372.68 197 49 8 760044 20150129 29.95 20151206 27.5 2 12.92 1440110509 QUEBEC 280 1.325 371 374 87 240 760833 20151202 2.99 20150511 2.89 82 1.223 1440306902 OTTAWA 80 5.885 470.8 264 107 48 760835 20151202 13.99 20150603 12.75 1160 5.326 1440512001 OTTAWA 4 9.74 38.96 78 0 70 760114 20150406 21.99 20151030 19.95 291 9.74 1442118502 QUEBEC 192 0.578 110.976 329 89 576 760461 20151108 1.59 20150722 1.49 1653 0.578 1442120001 OTTAWA 468 0.374 175.032 524 237 288 760923 20151203 1.09 20150731 0.99 3881 0.375 1446102008 QUEBEC 30 2.689 80.67 224 62 20 760766 20151202 6.49 20150910 5.95 294 2.514 1450163105 OTTAWA 210 2.6 546 368 184 280 760768 20151202 5.75 20100909 4.95 899 2.6 1460223010 QUEBEC 408 0.972 396.576 326 138 1344 760333 20150922 2.39 20150121 2.25 575 0.972 1460224017 QUEBEC 456 1.092 497.952 590 195 648 760373 20151010 2.79 20101206 2.65 4644 1.092 1480100107 QUEBEC 0 6 0 175 88 48 760263 20150810 12.95 20150831 0 16 5.46 1480300100 QUEBEC 126 0.825 103.95 176 63 216 760311 20150915 1.65 20150823 0 25 0.72 1480400106 OTTAWA 192 1.691 324.672 154 52 504 760258 20150807 3.95 20101218 0 879 1.56 1611113208 QUEBEC 1026 1.485 1523.61 896 423 1440 760134 20150421 3.49 20150520 2.89 931 1.43 1611119009 OTTAWA 420 0.892 374.64 336 118 360 760924 20151203 2.15 20100909 1.99 4155 0.906 1612700509 QUEBEC 1164 1.017 1183.788 1081 491 480 760559 20151117 2.45 20101228 0 9798 0.956 1612702006 OTTAWA 304 1.081 328.624 318 84 176 760836 20151202 2.59 20150317 2.29 3474 1.014 1612802500 QUEBEC 104 10.732 1116.128 183 17 42 760837 20151202 25.45 20150907 23.95 330 10.752 1612805205 TORONTO 2544 3.098 7881.312 2185 993 2160 760403 20151025 6.99 20150701 5.99 2250 3.032 1612833413 QUEBEC 348 3.055 1063.14 353 102 320 760498 20151113 6.49 20150621 5.99 5836 2.768 1616111609 QUEBEC 60 0.51 30.6 248 124 120 760563 20151117 1.35 20150123 0 4340 0.54 1620104508 TORONTO 1560 4.206 6561.36 1473 737 1728 760838 20151202 9.49 20150102 8.75 912 4.206 1620108018 TORONTO 840 0.915 768.6 822 361 1980 760297 20150911 2.59 20101025 0 7936 0.915 1620113801 OTTAWA 216 1.23 265.68 398 99 132 760770 20151202 2.99 20150519 2.49 11199 1.16 1621101123 TORONTO 576 2.076 1195.776 486 143 576 760839 20151202 4.75 20101215 4.45 691 1.94 1621102506 QUEBEC 140 6.41 897.4 187 69 118 760723 20151129 14.95 20151124 13.95 665 6.41 1621104109 QUEBEC 348 2.206 767.688 428 214 336 760282 20150828 5.29 20151108 4.95 79 1.986 1622100504 QUEBEC 432 0.549 237.168 471 160 540 760499 20151113 1.39 20150420 1.19 5653 0.515 1622101615 OTTAWA 924 1.05 970.2 839 370 1008 760252 20150731 2.25 20150411 2.09 306 0.968 1623225001 QUEBEC 408 2.794 1139.952 326 138 264 705108 20081124 5.75 20101012 5.45 4676 2.529 1624106169 QUEBEC 504 0.818 412.272 478 189 216 760607 20151121 1.99 20150420 0.99 0 0.818 1625103809 TORONTO 648 2.069 1340.712 643 322 432 760500 20151113 4.49 20150413 3.95 1677 1.929 1626109117 OTTAWA 300 1.344 403.2 290 95 372 760501 20151113 2.99 20150911 2.69 4670 1.283 1626124302 TORONTO 252 1.951 491.652 427 113 216 760502 20151113 4.75 20150609 4.45 5075 1.92 1626125003 TORONTO 540 0.785 423.9 632 241 360 760608 20151121 1.79 20101206 1.69 922 0.737 1626203006 QUEBEC 1824 0.804 1466.496 1509 680 504 760840 20151202 1.99 20150211 1.69 7043 0.727 1635560001 OTTAWA 108 0.778 84.024 136 18 120 760628 20151121 1.75 20150401 1.55 4 0.76 1650112010 QUEBEC 324 0.966 312.984 484 217 228 760644 20151124 2.39 20101029 2.29 2654 0.903 1650124019 TORONTO 297 1.16 344.52 288 94 216 760531 20151115 2.29 20151009 2.15 197 1.027 1650130014 QUEBEC 43 2.11 90.73 Y 184 42 50 760162 20150519 5.45 20150429 4.95 477 2.11 1650138012 QUEBEC 168 0.574 96.432 359 180 480 760272 20150816 1.39 20150718 1.29 5699 0.58 1710684014 OTTAWA 90 0.38 34.2 172 61 60 760925 20151203 1.09 20150525 0 1427 0.369 1710686516 TORONTO 50 0.27 13.5 165 0 60 760926 20151203 1.09 20150825 0 391 0.333 1710815309 OTTAWA 80 1.022 81.76 289 145 20 760927 20151203 2.79 20150416 2.59 1307 1.022 1710825506 QUEBEC 270 0.379 102.33 241 96 180 760988 20151204 1.09 20101110 0.99 3986 0.419 1710826006 QUEBEC 380 0.596 226.48 404 152 150 760989 20151204 1.65 20101113 1.59 4563 0.677 1710829008 OTTAWA 85 2.234 189.89 143 47 50 760990 20151204 5.45 20150310 4.95 64 2.03 1710833802 OTTAWA 50 0.739 36.95 165 0 120 760521 20151114 1.89 20150819 1.75 1135 0.68 1710834800 QUEBEC 220 0.692 152.24 351 76 150 760991 20151204 1.79 20150804 1.75 2713 0.62 1710857007 QUEBEC 70 1.21 84.7 81 0 60 760992 20151204 2.95 20151001 2.65 148 1.13 1710857504 QUEBEC 120 1.74 208.8 196 73 240 760409 20151025 4.79 20151128 4.29 561 1.62 1710857705 TORONTO 35 5.205 182.175 103 27 20 760725 20151129 12.45 20151110 11.5 49 4.68 1711401828 QUEBEC 0 0.556 0 Y 125 13 20 760704 20151128 1.49 20150204 0 687 0.54 1712223509 QUEBEC 252 1.816 457.632 327 138 240 760928 20151203 3.59 20150719 3.25 2020 1.754 1720108900 TORONTO 20 0.635 12.7 116 8 120 760780 20151202 1.95 20101112 1.75 661 0.677 1720151814 QUEBEC 36 0.844 30.384 104 2 36 761042 20151205 2.35 20101017 0 206 0.81 1720160113 QUEBEC 36 0.849 30.564 129 0 36 760645 20151124 2.35 20150331 0 537 0.81 1720162213 OTTAWA 70 0.97 67.9 256 53 80 760479 20151108 2.35 20151215 0 589 0.92 1720162719 TORONTO 54 1.422 76.788 168 59 24 760336 20150927 3.25 20101119 0 0 1.36 1720164104 OTTAWA 25 1.813 45.325 145 48 35 760332 20150921 4.49 20100824 4.25 109 1.761 1720164409 TORONTO 75 1.84 138 235 18 35 760929 20151203 4.49 20150226 4.25 42 1.789 1720164802 OTTAWA 24 4.788 114.912 169 85 20 760930 20151203 11.95 20150125 10.95 32 4.628 1720520618 TORONTO 180 1.06 190.8 319 135 160 760646 20151124 2.99 20100906 0 685 1.06 1720534611 TORONTO 60 0.49 29.4 98 0 60 761043 20151205 1.25 20150711 0 1543 0.49 1720538219 QUEBEC 132 0.84 110.88 206 28 96 761044 20151205 2.35 20100924 0 372 0.84 1721611723 OTTAWA 180 0.327 58.86 244 122 50 760841 20151202 1.09 20150117 0.99 381 0.329 1721611731 QUEBEC 240 0.36 86.4 317 134 120 760705 20151128 1.19 20101020 1.09 3281 0.356 1721611924 QUEBEC 240 0.312 74.88 242 71 250 760706 20151128 1.09 20101114 0.99 3648 0.3 1721612416 OTTAWA 270 0.327 88.29 241 71 50 760842 20151202 1.09 20101005 0.99 1939 0.313 1721612503 QUEBEC 400 0.327 130.8 520 235 60 760843 20151202 1.09 20100823 0.99 4549 0.314 1721616005 TORONTO 80 0.519 41.52 289 95 120 760844 20151202 1.65 20150621 1.49 1923 0.494 1721617203 OTTAWA 40 0.487 19.48 132 66 50 760845 20151202 1.35 20101111 1.29 8241 0.475 1721661115 OTTAWA 162 0.8 129.6 155 2 60 761045 20151205 2.89 20150628 0 0 0.8 1721661316 QUEBEC 54 0.8 43.2 43 22 48 760647 20151124 2.89 20150422 0 35 0.8 1721680300 QUEBEC 140 4.995 699.3 187 69 60 761046 20151205 11.95 20101005 10.75 0 5.34 1721680701 QUEBEC 260 4.995 1298.7 408 129 120 761047 20151205 11.95 20150419 10.75 417 5.34 1721682217 QUEBEC 90 0.677 60.93 272 61 80 760648 20151124 1.69 20151101 0 292 0.677 1722161167 OTTAWA 0 0.327 0 Y 125 13 50 760323 20150918 1.09 20151112 0.99 403 0.306 1725043909 QUEBEC 110 1.742 191.62 263 57 40 761048 20151205 4.99 20150823 0 671 1.859 1725403311 OTTAWA 160 1.194 191.04 178 39 80 761049 20151205 3.95 20150426 0 0 1.276 1725405202 OTTAWA 390 0.792 308.88 387 194 40 761050 20151205 2.5 20151106 0 4485 0.846 1725406009 TORONTO 430 1.275 548.25 519 210 80 761051 20151205 3.5 20150918 0 7 1.364 1725436108 QUEBEC 0 0.515 0 Y 50 0 500 760782 20151202 1.65 20150622 1.5 5255 0.667 1725479203 OTTAWA 150 0.953 142.95 345 148 200 760711 20151128 2.75 20100903 0 0 0.927 1725497504 TORONTO 80 7.98 638.4 89 0 70 760931 20151203 19.49 20150214 17.95 421 7.77 1725497705 LONDON 38 5.503 209.114 55 28 14 760932 20151203 13.49 20101206 11.95 63 4.989 1725831509 QUEBEC 30 5.507 165.21 249 125 18 761052 20151205 13.25 20151213 11.95 321 5.6 1725832506 QUEBEC 20 4.422 88.44 141 0 8 761053 20151205 10.75 20100928 9.75 188 4.45 1725833006 QUEBEC 28 4.21 117.88 222 111 12 761054 20151205 9.95 20150623 8.95 213 4.21 1725901205 QUEBEC 48 3.953 189.744 238 94 36 761055 20151205 10.29 20150607 0 602 4.225 1725902001 TORONTO 50 1.492 74.6 90 0 50 760597 20151119 5.29 20150323 0 116 1.594 1725902202 TORONTO 52 1.699 88.348 42 0 24 761056 20151205 5.79 20151222 0 180 1.815 1725903601 TORONTO 75 2.379 178.425 110 0 80 760784 20151202 7.95 20101231 0 377 2.536 1725904408 QUEBEC 256 3.064 784.384 205 27 180 761057 20151205 9.29 20150124 0 3186 3.275 1725905509 QUEBEC 56 3.4 190.4 195 0 36 761058 20151205 10.29 20150607 0 400 3.4 1725908109 TORONTO 175 0.529 92.575 215 108 60 761059 20151205 1.89 20150528 0 127 0.566 1725908407 QUEBEC 210 2.763 580.23 393 172 160 760786 20151202 8.95 20101018 0 1712 2.953 1725909108 TORONTO 230 1.285 295.55 409 155 180 760788 20151202 2.95 20151003 0 415 1.374 1725909309 OTTAWA 50 1.242 62.1 215 8 60 761062 20151205 4.29 20151120 0 1335 1.326 1725910107 OTTAWA 30 0.798 23.94 99 50 80 760526 20151114 2.59 20101020 0 966 0.852 1725910308 QUEBEC 20 0 0 41 0 80 760790 20151202 2.79 20101124 0 156 1.068 1725911104 OTTAWA 16 5.662 90.592 138 44 12 761063 20151205 13.95 20151013 0 748 6.05 1725911707 OTTAWA 72 9.039 650.808 83 0 84 760792 20151202 18.49 20150709 0 134 9.66 1725913109 OTTAWA 130 0.442 57.46 129 0 90 761064 20151205 1.25 20150108 0 361 0.472 1725913405 TORONTO 100 0.477 47.7 205 28 155 760377 20151013 1.49 20150109 0 3795 0.508 1725915209 OTTAWA 115 0.854 98.21 117 9 25 761065 20151205 2.49 20150619 0 952 0.912 1725915801 QUEBEC 30 0.854 25.62 174 12 75 760794 20151202 2.49 20150223 0 510 0.912 1725915907 QUEBEC 105 0.854 89.67 259 105 75 760527 20151114 2.49 20150428 0 97 0.912 1725916407 OTTAWA 20 0.567 11.34 41 0 225 760598 20151119 1.75 20150307 0 21 0.606 1725917944 TORONTO 68 4.154 282.472 54 27 60 760599 20151119 10.95 20150519 0 823 4.44 1730610203 QUEBEC 300 1.925 577.5 465 183 225 761066 20151205 4.25 20150401 3.99 790 1.896 1730615009 QUEBEC 65 2.843 184.795 152 51 60 761067 20151205 6.49 20150314 5.95 1279 2.824 1730616006 QUEBEC 180 1.174 211.32 269 35 150 761068 20151205 2.85 20150924 2.69 861 1.199 1730623009 OTTAWA 135 3.281 442.935 233 42 40 761069 20151205 6.95 20150724 6.45 2765 3.271 1730624006 QUEBEC 140 3.901 546.14 187 94 60 760772 20151202 7.95 20101022 6.95 521 3.891 1730629002 OTTAWA 85 9.999 849.915 118 9 35 761070 20151205 21.95 20150609 19.95 553 9.957 1730630001 QUEBEC 85 3.614 307.19 118 34 45 761071 20151205 7.79 20151009 7.25 9 3.572 1730642009 OTTAWA 150 2.082 312.3 170 35 180 760774 20151202 4.79 20150507 4.69 1939 2.084 1730691416 TORONTO 162 5.792 938.304 330 165 180 760528 20151114 13.79 20150705 11.95 547 5.287 1730695415 OTTAWA 90 4.924 443.16 122 0 180 760529 20151114 11.59 20150626 10.35 1126 4.429 1730696709 TORONTO 102 3.029 308.958 232 0 372 760378 20151013 6.99 20151024 6.29 920 2.685 1730714400 OTTAWA 90 3.406 306.54 222 111 60 761072 20151205 7.99 20150729 7.65 119 3.262 1730718809 QUEBEC 171 14.129 2416.059 212 31 150 266 20151005 24.95 20150802 33.95 4219 14.129 1730719100 QUEBEC 26 11.99 311.74 246 73 24 761073 20151205 28.95 20150327 27.95 229 11.746 1730719309 TORONTO 24 13.142 315.408 44 22 8 761074 20151205 31.89 20151108 30.75 329 12.489 1730721009 QUEBEC 35 5.051 176.785 103 52 30 760434 20151102 12.29 20150501 11.95 602 5.007 1730723005 TORONTO 39 11.592 452.088 256 28 20 761075 20151205 25.95 20101225 25.49 10 11.587 1730723407 OTTAWA 29 14.425 418.325 73 12 20 761076 20151205 35.95 20150826 33.95 139 14.042 1730723904 TORONTO 14 21.907 306.698 236 18 40 760483 20151109 49.95 20150903 48.5 295 21.588 1730730400 QUEBEC 42 1.539 64.638 209 54 72 760776 20151202 3.59 20150824 3.29 266 1.321 1730733209 TORONTO 80 7.838 627.04 264 107 60 760532 20151115 18.95 20150827 17.95 1264 6.758 1730733602 QUEBEC 0 4.729 0 100 0 36 760656 20151124 11.99 20150329 10.75 1719 4.103 1730734007 QUEBEC 86 16.407 1411.002 194 97 120 760435 20151102 34.95 20151224 37.95 535 14.608 1730820018 TORONTO 140 1.394 195.16 112 6 100 761077 20151205 3.39 20150102 3.29 814 1.33 1730820042 QUEBEC 195 2.334 455.13 381 166 150 760778 20151202 5.69 20100928 5.49 1768 2.22 1730882115 OTTAWA 220 0.531 116.82 201 1 360 760933 20151203 1.95 20101027 0 1316 0.522 1731265218 OTTAWA 49 10.131 496.419 64 7 24 761078 20151205 19.49 20150118 16.95 112 8.697 1731265419 QUEBEC 180 4.839 871.02 194 72 180 760600 20151119 8.49 20150515 7.89 831 4.239 1731610407 TORONTO 104 7.67 797.68 133 67 48 760993 20151204 15.95 20151212 14.75 530 7.34 1731618003 QUEBEC 3420 2.6 8892 2861 1331 5120 760727 20151129 3.99 20150124 3.49 129306 0 1740201405 QUEBEC 11 13.757 151.327 184 67 12 760323 20150918 62.4 20151103 54 142 12.834 1740204206 OTTAWA 132 1.421 187.572 306 78 96 760934 20151203 3.09 20150730 3.99 1244 1.346 1740210402 QUEBEC 60 0 0 273 137 80 760363 20151005 1.09 20101028 0.99 728 0.356 1740210508 TORONTO 190 0.376 71.44 352 101 240 760508 20151113 1.09 20101219 0.99 341 0.359 1740407000 QUEBEC 30 0.391 11.73 49 0 160 760379 20151013 1.19 20150723 1.09 924 0.344 1750430905 QUEBEC 190 0.971 184.49 152 0 120 760729 20151129 2.25 20151211 0 778 0.915 1750431405 OTTAWA 215 0.8 172 322 136 100 760730 20151129 1.99 20150807 1.79 602 0.718 1750431606 OTTAWA 30 0.391 11.73 249 125 200 760649 20151124 0.99 20150215 0.85 281 0.34 1750432106 OTTAWA 170 0.3 51 286 143 360 760650 20151124 0.75 20150720 0.65 282 0.269 1750432201 OTTAWA 0 0.42 0 75 0 210 703755 20080223 1.15 20150522 0 0 0 1760111009 QUEBEC 60 1.402 84.12 248 74 80 760935 20151203 3.99 20101222 0 3380 1.524 1780217503 QUEBEC 100 0.884 88.4 305 53 320 760651 20151124 1.99 20150405 1.79 1730 0.819 1790501508 TORONTO 60 0.521 31.26 273 87 108 760680 20151126 0.99 20101222 1.09 2147 0.525 1790501709 QUEBEC 0 0.524 0 25 0 108 760492 20151111 0.99 20151207 1.09 2710 0.525 1790502008 QUEBEC 120 0.514 61.68 221 11 108 760681 20151126 0.99 20151215 1.25 669 0.525 1810101007 OTTAWA 120 1.495 179.4 96 48 20 761121 20151217 3.75 20101128 3.25 276 1.497 1810105905 QUEBEC 10 0.897 8.97 Y 108 29 480 761089 20151206 1.95 20151012 1.75 1578 0.805 1890107401 TORONTO 10800 1.35 14580 12000 1000 600 704498 20081112 1.99 20150206 0 50463 1.35 1890107602 OTTAWA 7360 1.35 9936 6000 3000 200 705179 20081205 1.99 20150330 0 24937 1.35 1930410107 QUEBEC 0 0.604 0 175 88 1368 704284 20080525 0.49 20150128 0.99 0 0.38 1930619000 QUEBEC 0 0.67 0 50 0 1440 704491 20081018 0.79 20100916 1.59 0 0.63 1930620305 TORONTO 0 0.72 0 50 0 648 704492 20081018 0.89 20151212 1.79 84 0.7 1930903402 OTTAWA 0 8.7 0 25 0 81 704493 20081102 9.99 20150520 19.99 0 9.2 1933300707 OTTAWA 0 0.4 0 125 13 528 760312 20150915 0.49 20151116 0.99 0 0.4 1940100158 QUEBEC 0 6.378 0 100 25 882 760282 20150828 4.97 20150507 9.95 0 6 1950100809 OTTAWA 0 0.56 0 25 0 1280 760260 20150807 0.58 20101129 1.15 0 0.56 1950101002 TORONTO 0 0.455 0 175 88 4632 760282 20150828 0.47 20150905 0.95 0 0.455 1950102909 QUEBEC 0 0.75 0 125 63 1152 760255 20150804 0.73 20150327 1.45 0 0.75 2260100702 TORONTO 564 5.73 3231.72 451 151 400 760731 20151129 9.29 20150130 8.99 6026 5.652 2260394007 OTTAWA 24 0.34 8.16 Y 19 0 108 760294 20150908 0.95 20151113 0 830 0.34 2270610004 QUEBEC 612 2.48 1517.76 690 245 480 760636 20151122 5.95 20150831 5.29 4418 2.981 2270905109 OTTAWA 2172 0.728 1581.216 1738 794 696 760936 20151203 1.39 20150225 1.29 5425 0.75 2272300101 TORONTO 1384 2.324 3216.416 1282 641 1248 760937 20151203 4.79 20150510 3.99 22097 2.605 2272303505 OTTAWA 2148 0.56 1202.88 1793 897 1152 760732 20151129 1.99 20150326 1.59 532 0.519 2429000515 OTTAWA 0 1.935 0 25 13 320 760064 20150228 4.75 20150911 0 0 0 2429003001 QUEBEC 260 1.77 460.2 333 67 180 760846 20151202 4.15 20150201 4.45 0 0 2429005511 QUEBEC 0 0.495 0 50 25 480 760103 20150402 1.19 20101122 0 0 0 2429008810 QUEBEC 0 1.16 0 0 0 768 760125 20150415 1.75 20151208 2.69 0 0 2910608500 OTTAWA 0 27.475 0 225 88 190 704276 20080514 50.99 20101107 46.95 662 26.4 2910623809 QUEBEC 45 29.52 1328.4 136 0 40 760652 20151124 59.88 20150103 54.59 68 28.39 2920601206 QUEBEC 712 3.549 2526.888 570 210 288 760205 20150706 7.49 20150512 6.99 11933 3.549 2970101604 QUEBEC 720 0.947 681.84 601 301 240 760355 20151004 2.99 20101016 2.85 1698 0.965 2970101805 QUEBEC 300 0.634 190.2 315 133 140 760653 20151124 2.25 20101010 2.15 4986 0.604 2970102908 QUEBEC 360 1.125 405 363 107 50 760756 20151130 2.85 20150119 2.75 1916 1.128 3010241505 OTTAWA 170 0.563 95.71 211 56 140 760426 20151029 1.69 20101220 0 817 0.563 3011004509 TORONTO 276 8.52 2351.52 221 35 70 760938 20151203 18.95 20101216 16.95 1580 9.66 3011501001 QUEBEC 160 0.94 150.4 153 2 60 760994 20151204 2.59 20150609 0 2478 0.9 3011509000 OTTAWA 70 1.119 78.33 206 53 50 760573 20151118 2.59 20150916 0 1279 1.04 3011513009 QUEBEC 60 0.997 59.82 248 99 190 760574 20151118 2.75 20150329 2.5 1421 0.91 3011515005 TORONTO 100 4.53 453 180 65 140 760637 20151122 9.65 20150102 10.75 2934 4.22 3011516500 OTTAWA 150 0.859 128.85 120 60 80 760995 20151204 1.75 20150305 2.25 486 0.8 3020200302 QUEBEC 19 3.247 61.693 40 0 24 760996 20151204 8.95 20150421 4.5 850 3.268 3020200704 QUEBEC 129 7.779 1003.491 178 64 89 760997 20151204 15.95 20101115 8.95 372 7.358 3030243000 QUEBEC 1090 0.28 305.2 1022 436 50 760847 20151202 1.05 20150322 0 252 0.32 3030243507 OTTAWA 480 0.325 156 584 267 800 760682 20151126 1.15 20150111 0 3003 0.37 3030245501 QUEBEC 200 0.757 151.4 360 155 50 760683 20151126 2.35 20150209 0 4669 0.86 3040301208 OTTAWA 120 0.945 113.4 146 48 30 760733 20151129 2.39 20150520 2.2 1594 0.922 3040301600 QUEBEC 90 1.41 126.9 197 99 40 760998 20151204 3.85 20150603 3.5 478 1.415 3040302406 QUEBEC 70 1.575 110.25 156 0 50 760999 20151204 3.85 20101011 3.5 417 1.489 3040302607 QUEBEC 10 1.938 19.38 33 0 20 761000 20151204 4.95 20150411 4.5 507 1.927 3040303604 TORONTO 80 2.011 160.88 164 82 40 760326 20150919 4.94 20100828 4.5 906 1.929 3040305207 TORONTO 20 2.005 40.1 41 0 20 760375 20151011 4.95 20100904 4.45 2755 1.927 3040306003 QUEBEC 100 1.946 194.6 230 115 100 761001 20151204 4.95 20150512 4.5 687 1.927 3040309608 TORONTO 310 1.099 340.69 473 187 480 760734 20151129 2.75 20100913 2.45 6490 1.068 3040310004 QUEBEC 50 2.034 101.7 115 58 70 760469 20151108 4.95 20151205 4.45 120 1.927 3040503702 TORONTO 40 1.537 61.48 232 16 20 760470 20151108 3.95 20151029 3.5 1043 1.531 3040505104 QUEBEC 90 1.846 166.14 222 86 70 761002 20151204 3.85 20150422 4.35 711 1.625 3040615000 QUEBEC 770 0.414 318.78 841 421 640 761003 20151204 1.29 20150104 1.15 9728 0.447 3040621407 QUEBEC 60 2.296 137.76 148 74 50 760376 20151011 7.95 20150103 7.15 249 2.496 3040622002 OTTAWA 50 1.52 76 240 70 30 761004 20151204 5.25 20150210 4.69 284 1.65 3040623209 OTTAWA 25 1.488 37.2 145 48 5 761005 20151204 4.75 20150326 3.85 513 1.562 3040624809 OTTAWA 110 0.696 76.56 88 44 100 760471 20151108 2.19 20150923 1.95 1932 0.742 3040907506 OTTAWA 15 3.031 45.465 62 0 48 760735 20151129 8.95 20150405 7.95 253 3.194 3050400909 TORONTO 5 1.92 9.6 179 0 250 760575 20151118 5.25 20151230 4.75 414 1.954 3060801006 TORONTO 135 2.412 325.62 283 92 135 760576 20151118 5.95 20150312 0 474 2.153 3060806500 OTTAWA 138 3.82 527.16 235 118 114 761006 20151204 8.95 20150804 0 241 3.455 3061902500 QUEBEC 0 1.45 0 150 0 320 705156 20081125 3.25 20150406 0 2182 1.463 3062001106 OTTAWA 1092 1.97 2151.24 999 424 768 761007 20151204 4.75 20150102 4.45 1047 2.206 3062006007 OTTAWA 375 7.44 2790 500 200 144 761008 20151204 16.95 20100902 19.95 3057 7.2 3121206003 QUEBEC 450 2.59 1165.5 585 218 200 760364 20151006 5.99 20150224 0 173 2.59 3121206300 QUEBEC 50 2.59 129.5 65 0 50 760577 20151118 5.99 20150618 0 218 2.59 3131300401 QUEBEC 0 9.16 0 75 0 16 760265 20150810 20.35 20101102 20.99 621 8.4 3131301208 QUEBEC 0 9.16 0 125 0 80 760306 20150914 20.35 20150329 20.49 0 8.4 3131301409 OTTAWA 0 11.34 0 175 0 80 760275 20150823 24.45 20150315 24.49 753 10.4 3131303403 QUEBEC 0 0.709 0 125 63 300 760277 20150823 1.75 20101119 1.65 1444 0.65 3131304009 QUEBEC 0 0.65 0 0 0 150 760320 20150917 1.75 20150311 1.65 3180 0.65 3140422307 OTTAWA 26 8.159 212.134 46 0 24 761009 20151204 21.99 20150121 20.15 1026 8.153 3140422508 QUEBEC 32 10.62 339.84 176 0 48 760736 20151129 27.99 20150924 27.65 289 10.62 3140423209 OTTAWA 52 13.76 715.52 92 0 44 760578 20151118 34.99 20150107 34.75 24 13.759 3140423400 QUEBEC 8 19.67 157.36 206 0 10 761010 20151204 49.99 20151018 50.25 33 19.652 3140423706 QUEBEC 20 1.958 39.16 191 46 120 760013 20081214 5.49 20150222 4.99 2118 1.957 3210152005 TORONTO 2583 0.147 379.701 2800 1000 3969 760412 20151026 0.39 20150522 0.35 12025 0.138 3220200505 OTTAWA 63 2.27 143.01 75 13 70 760737 20151129 3.29 20101113 3.11 665 1.7 3240120706 QUEBEC 130 1.263 164.19 179 40 150 760343 20150928 3.59 20100823 0 31 1.079 3250102409 QUEBEC 231 1.54 355.74 285 42 432 760564 20151117 2.95 20150724 2.75 3009 1.391 3250111808 OTTAWA 474 1.18 559.32 379 115 216 760713 20151128 2.95 20100907 2.65 962 1.317 3250136000 QUEBEC 324 1.12 362.88 334 92 264 760715 20151128 2.6 20151002 2.5 1071 1.105 3250149609 QUEBEC 198 0.71 140.58 358 104 144 760939 20151203 2.19 20150616 1.99 2173 0.71 3250149800 QUEBEC 264 1.45 382.8 261 106 264 760717 20151128 3.49 20150823 3.25 1368 1.513 3250170100 QUEBEC 570 1.72 980.4 531 216 54 760719 20151128 4.49 20101012 3.95 379 1.925 3250192008 QUEBEC 78 2.964 231.192 87 0 72 760629 20151121 7.55 20150501 6.99 447 3.703 3415507507 OTTAWA 3514 4.415 15514.31 2911 1431 1120 760657 20151124 10.99 20150715 9.99 3630 5 3416101011 OTTAWA 335 17.849 5979.415 268 109 800 760153 20150505 29.95 20101031 34.95 4913 17.849 3416403705 OTTAWA 11 128.436 1412.796 134 42 10 761079 20151205 249 20150319 0 181 121.8 3416403906 QUEBEC 986 22.661 22343.746 914 407 400 760472 20151108 44.99 20151124 0 573 21.48 3416405158 OTTAWA 14 27.58 386.12 61 0 14 760416 20151028 54.99 20150907 0 5057 27.58 3416406305 QUEBEC 2120 4.58 9709.6 1896 923 920 760940 20151203 8.99 20101107 0 37519 4.58 3416406557 QUEBEC 685 14.929 10226.365 573 187 100 760941 20151203 34.99 20151028 0 2966 14.92 3416502006 TORONTO 100 83.645 8364.5 280 140 111 760116 20150406 189 20150818 0 658 83.645 3421004669 OTTAWA 170 6.087 1034.79 Y 361 81 150 760316 20150917 14.95 20150313 0 2130 6.58 3421004766 OTTAWA 330 4.08 1346.4 Y 289 45 320 760317 20150917 8.95 20150525 0 6169 4.11 3422600157 QUEBEC 1584 0.628 994.752 1467 634 1080 760533 20151115 1.25 20150628 0 4571 0.59 3425017006 OTTAWA 240 0.382 91.68 192 0 300 760321 20150917 0.85 20150129 0.75 0 0.397 3425028008 QUEBEC 300 3.6 1080 290 120 648 760016 20081228 6.95 20151002 6.45 16815 3.6 3425030004 OTTAWA 5460 0.499 2724.54 4543 2272 3360 760043 20150202 0.95 20151020 0.89 16248 0.46 3431004455 OTTAWA 92 3.076 282.992 99 49 288 760313 20150915 5.25 20150509 0 125 2.7 3431006555 QUEBEC 810 1.999 1619.19 698 299 258 760658 20151124 3.99 20150614 0 657 2.1 3670300302 QUEBEC 69 8.499 586.431 230 0 60 761011 20151204 18.95 20151227 16.95 555 8.008 3730135000 QUEBEC 1368 2.11 2886.48 1294 600 216 760410 20151025 4.99 20150912 5.49 57 2.138 3740110008 QUEBEC 1900 1.02 1938 1570 685 1000 760942 20151203 2.75 20150329 2.45 8150 1.223 3810145801 OTTAWA 918 12.127 11132.586 859 330 576 760473 20151108 19.99 20150627 0 20 11.29 3810510003 TORONTO 51 44.557 2272.407 41 0 21 760484 20151109 95.99 20150911 84.95 263 42.514 3810512008 QUEBEC 110 14.967 1646.37 288 50 120 760485 20151109 27.99 20150321 24.95 699 14.28 3810700107 OTTAWA 99 13.5 1336.5 229 15 100 760943 20151203 26.99 20101105 24.95 0 0 3810915005 QUEBEC 119 10.327 1228.913 245 10 120 760545 20151115 14.95 20150814 21.95 0 0 3811300104 TORONTO 0 4.95 0 125 0 108 760455 20151106 10.99 20151224 9.95 992 4.95 3811313005 QUEBEC 45 2.75 123.75 261 81 150 760489 20151109 6.99 20150310 5.95 1337 2.817 3811355008 QUEBEC 4 126.54 506.16 53 27 12 760383 20151015 209 20101229 199 125 126.54 3811500107 TORONTO 5 4.364 21.82 79 0 800 760381 20151015 9.45 20150902 8.95 0 0 4110134001 QUEBEC 310 0.28 86.8 373 100 320 761122 20151217 0.69 20150828 0.65 582 0.242 4110182000 TORONTO 270 0.28 75.6 316 100 170 761123 20151217 0.79 20150314 0 33730 0.28 4110182156 TORONTO 310 0.38 117.8 448 100 140 761130 20151218 0.99 20100902 0 4397 0.38 4110182452 QUEBEC 250 0.65 162.5 400 200 225 761131 20151218 1.79 20150907 0 8283 0.65 4110182708 QUEBEC 650 0.33 214.5 745 373 400 761104 20151216 0.99 20100911 0 11650 0.33 4110182759 OTTAWA 300 0.69 207 440 195 252 761105 20151216 1.99 20101118 0 41496 0.69 4110301601 QUEBEC 468 0.95 444.6 374 187 576 761092 20151208 2.75 20150511 2.45 0 0.9 4120105707 OTTAWA 780 1.6 1248 649 300 936 761094 20151209 3.95 20150927 2.95 0 1.345 4120115342 QUEBEC 38 5.47 207.86 205 53 36 761106 20151216 11.99 20100823 0 97 5.04 4120118089 TORONTO 176 3.83 674.08 216 58 50 761120 20151217 9.99 20100829 8.56 159 3.63 4120150087 QUEBEC 156 1.9 296.4 175 12 12 761132 20151218 4.5 20150815 0 1697 1.76 4120150166 QUEBEC 16 6.16 98.56 138 19 96 761096 20151212 15.49 20150719 13.99 387 5.72 4120141304 QUEBEC 6 1.58 9.48 55 2 10 761101 20151214 4.45 20151217 4.95 7 1.85 4120148009 OTTAWA 30 4.36 130.8 124 0 147 761090 20151207 10.95 20151005 9.95 427 4.03 4120151002 TORONTO 24 3.47 83.28 194 97 66 761098 20151213 8.95 20151006 7.95 605 3.23 4120181207 QUEBEC 64 3.79 242.56 276 38 48 761102 20151214 7.95 20101021 6.95 324 3.49 4120181800 QUEBEC 44 4.42 194.48 185 68 72 761107 20151216 9.95 20151006 9.5 535 4.21 4120199514 QUEBEC 96 6.75 648 152 0 72 761108 20151216 15.99 20150315 0 67 6.75 4120438405 OTTAWA 84 7.39 620.76 267 0 64 761109 20151216 16.95 20100901 15.95 148 7.157 4120602506 QUEBEC 32 2.95 94.4 201 75 36 761133 20151218 6.95 20151121 6.45 658 2.95 4120700508 OTTAWA 58 6.95 403.1 146 73 48 761110 20151216 14.95 20101009 13.95 1469 6.493 4127501319 QUEBEC 12 11.7 140.4 210 80 9 761124 20151217 27.95 20150902 24.95 210 0 4127503313 OTTAWA 140 1.38 193.2 337 169 63 761111 20151216 3.45 20150203 0 1083 0 4127504110 QUEBEC 43 7.874 338.582 59 30 32 761125 20151217 17.95 20150719 0 236 0 4210105006 TORONTO 60 4.91 294.6 148 0 40 761112 20151216 11.99 20150821 10.99 129 4.376 4210115035 TORONTO 27 5.5 148.5 97 23 8 761134 20151218 9.99 20150402 10.99 231 4.59 4210125005 OTTAWA 63 2.87 180.81 125 0 45 761135 20151218 6.99 20150926 6.25 1214 2.61 4210125502 QUEBEC 87 2.87 249.69 195 72 45 761136 20151218 6.99 20150815 6.25 270 2.714 4210129004 OTTAWA 78 0.8 62.4 212 56 63 761137 20151218 1.75 20101019 1.45 791 0.637 4210133005 OTTAWA 15 1.55 23.25 112 56 36 761126 20151217 3.59 20101231 3.25 280 1.349 4210137745 QUEBEC 12 9.65 115.8 235 117 24 761127 20151217 23.95 20151208 22.95 307 8.531 4210403009 TORONTO 42 9.95 417.9 159 0 30 761093 20151209 22.95 20150904 0 107 8.9 4210403201 TORONTO 15 13.9 208.5 237 0 20 761113 20151216 29.95 20150525 0 59 13.9 4210405021 QUEBEC 54 8.95 483.3 118 59 60 761099 20151213 18.95 20150205 19.95 0 8.252 4210405030 QUEBEC 30 12.9 387 99 0 20 761139 20151218 26.95 20101111 0 1 10.9 4312003509 QUEBEC 314 7.169 2251.066 251 26 46 761012 20151204 10.99 20150324 10.29 2079 6.581 4312015009 OTTAWA 1 9.808 9.808 176 0 6 760363 20151005 14.75 20151109 0 90 9 4321023001 QUEBEC 0 3.02 0 Y 25 0 144 760357 20151003 6.95 20150415 5.95 866 3.013 4321024506 OTTAWA 0 1.97 0 Y 0 0 160 760546 20151115 4.95 20100912 4.25 1576 1.969 4321025503 QUEBEC 0 3.1 0 50 0 81 760490 20151109 6.95 20150506 5.95 1514 3.1 4321028008 TORONTO 0 3.29 0 25 0 94 760547 20151115 10.95 20150212 9.95 2354 3.29 4321040009 QUEBEC 33 2.43 80.19 51 26 54 760548 20151115 5.15 20100912 0 1436 2.43 4322006504 QUEBEC 186 0.369 68.634 249 24 90 760944 20151203 0.99 20150225 0.89 359 0.353 4322010008 OTTAWA 720 0.663 477.36 751 326 400 760945 20151203 1.55 20150518 0 892 0.663 4323011008 QUEBEC 90 3.417 307.53 222 86 100 760396 20151023 7.95 20150601 0 274 3.25 4323030000 QUEBEC 216 1 216 173 11 180 760565 20151117 2.99 20150830 2.89 892 0.89 4323040004 OTTAWA 92 3.298 303.416 149 0 200 760530 20151114 7.65 20101017 0 2741 3 4324010509 QUEBEC 84 0.426 35.784 67 0 180 760417 20151028 1.09 20151218 0.95 1975 0.413 4324035009 TORONTO 0 1.263 0 100 0 162 760278 20150823 2.95 20100919 0 540 1.17 4326008500 QUEBEC 642 0.467 299.814 589 0 108 760946 20151203 1.3 20151105 1.2 3977 0.484 4332005109 QUEBEC 90 2.572 231.48 Y 122 0 120 760522 20151114 4.99 20150113 5.99 682 2.572 4351001506 TORONTO 170 0.588 99.96 236 68 300 760436 20151102 1.55 20101212 1.75 2165 0.541 4351002006 QUEBEC 370 0.586 216.82 521 186 600 760354 20151003 1.55 20101106 1.75 1245 0.533 4351004009 TORONTO 580 0.81 469.8 589 195 450 760413 20151026 1.89 20150702 1.75 1464 0.81 4351004508 OTTAWA 340 0.754 256.36 347 99 1800 760285 20150901 1.89 20150726 1.75 4032 0.81 4358000804 TORONTO 123 2.3 282.9 98 0 90 760758 20151130 5.99 20150203 3.99 1089 2.3 4358002407 TORONTO 66 2.3 151.8 53 0 108 760369 20151009 4.99 20150812 4.95 186 2.3 4358003002 QUEBEC 75 2.3 172.5 110 30 54 760760 20151130 4.99 20150817 4.95 513 2.3 4359001201 QUEBEC 66 2.81 185.46 103 1 216 760398 20151023 6.45 20150621 0 671 2.81 4359001402 OTTAWA 258 2.81 724.98 231 41 108 760549 20151115 6.45 20150518 0 55 2.81 4361002209 QUEBEC 28 12 336 197 74 96 760437 20151102 27.95 20151019 25.95 675 12 4361004808 TORONTO 165 3.465 571.725 232 66 72 760947 20151203 8.99 20151127 7.95 2585 3.194 4510105006 QUEBEC 70 3.79 265.3 90 30 48 761103 20151214 7.95 20101021 6.95 324 3.49 4510115035 QUEBEC 60 4.42 265.2 185 68 72 761114 20151216 9.95 20151006 9.5 535 4.21 4510125005 QUEBEC -2 6.75 -13.5 40 0 72 761115 20151216 15.99 20150315 0 67 6.75 4510125502 OTTAWA 90 7.39 665.1 260 0 64 761116 20151216 16.95 20100901 15.95 148 7.157 4510129004 QUEBEC 40 2.95 118 200 75 36 761140 20151218 6.95 20151121 6.45 658 2.95 4510133005 OTTAWA 60 6.95 417 90 30 48 761117 20151216 14.95 20101009 13.95 1469 6.493 4510137745 QUEBEC 15 11.7 175.5 180 80 9 761128 20151217 27.95 20150902 24.95 210 0 4510401006 OTTAWA 220 1.38 303.6 250 150 63 761118 20151216 3.45 20150203 0 1083 0 4510403009 QUEBEC 32 7.874 251.968 60 30 32 761129 20151217 17.95 20150719 0 236 0 4510403201 TORONTO 50 4.91 245.5 140 0 40 761119 20151216 11.99 20150821 10.99 129 4.376 4510405021 TORONTO 30 5.5 165 90 23 8 761141 20151218 9.99 20150402 10.99 231 4.59 4510405030 OTTAWA 90 2.87 258.3 125 0 45 761142 20151218 6.99 20150926 6.25 1214 2.61 4512003509 QUEBEC 94 2.87 269.78 195 72 45 761143 20151218 6.99 20150815 6.25 270 2.714 4512015009 OTTAWA 8 0.8 6.4 212 56 63 761144 20151218 1.75 20101019 1.45 791 0.637 4520700508 QUEBEC 54 6.16 332.64 60 19 96 761097 20151212 15.49 20150719 13.99 387 5.72 4527501319 QUEBEC 29 1.58 45.82 40 2 10 761095 20151212 4.45 20151217 4.95 7 1.85 4527503313 OTTAWA 40 4.36 174.4 130 0 147 761091 20151207 10.95 20151005 9.95 427 4.03 4527504110 TORONTO -5 3.47 -17.35 200 20 66 761100 20151213 8.95 20151006 7.95 605 3.23
IDEA Data Analysis Workbook/Source Files.ILB/Supplier.xls
Address
| SUPPNO | SUPPNAME | ADDRESS1 | ADDRESS2 | ADDRESS3 | ZIP_CODE | TOT_PREV_YR |
| A128 | Ivan Aker | 149 1 AVE. | OTTAWA | ONTARIO | K3P 3I0 | 480343.99 |
| B008 | Denise Bent | 43-191 67 AVE. | OTTAWA | ONTARIO | K8J 2S3 | 437119.23 |
| B010 | Carter Bout | 869 KENSIGNTON AVE. | TORONTO | ONTARIO | M6H 8D6 | 556865.35 |
| C202 | Cash Inc | 3124 TOWER ST. | OTTAWA | ONTARIO | P3Z 8J8 | 926888.88 |
| D014 | William Ditt | 151 MAGNUS AVE. | OTTAWA | ONTARIO | L2Q 0N7 | 390600.00 |
| D025 | Nellie Dunn | 957 QUEEN ST. | OTTAWA | ONTARIO | P2C 9G9 | 674092.45 |
| F123 | Wanda Farr | 599 23 E AVE. | OTTAWA | ONTARIO | P3V 4Z9 | 234102.71 |
| F128 | Fixes | 533 DOMINIC ST. | OTTAWA | ONTARIO | N0W 1L4 | 690799.89 |
| F130 | Farmer | 455 39 E AVE. | OTTAWA | ONTARIO | L1G 9B6 | 704603.56 |
| G010 | Polly Gunn | 141 58 ST. | OTTAWA | ONTARIO | N8C 3X5 | 0.00 |
| G020 | P Green | 72-930 QUEEN ST. | EDMONTON | ALBERTA | T5J 7M1 | 719875.68 |
| H014 | Linda Hand | 140 11 AVE. | OTTAWA | ONTARIO | N4F 7Q2 | 609855.94 |
| H025 | Luke Hair | 19-956 MAIN ST. | OTTAWA | ONTARIO | K5V 3X1 | 644588.82 |
| K001 | O Kay Yahs | 635 59 AVE. | OTTAWA | ONTARIO | P0Z 5K7 | 998678.23 |
| M014 | Cary S Matic | 67 62 S AVE. | OTTAWA | ONTARIO | L0Y 5T3 | 695843.17 |
| M020 | Miles Long | 556 24 ST. | TORONTO | ONTARIO | M1T 7S4 | 718320.63 |
| M025 | A Meadow | 731 83 AVE. | OTTAWA | ONTARIO | P9H 4E8 | 1217309.35 |
| M123 | 1 Moore | 336 KING ST. | OTTAWA | ONTARIO | P0A 7B1 | 490890.65 |
| M128 | Gus Main | 45-333 12 S AVE. | OTTAWA | ONTARIO | N9W 1I7 | 800678.87 |
| M130 | Maurice Mynah | 332 RIVERSIDE DRIVE | OTTAWA | ONTARIO | L9V 6P4 | 992725.50 |
| N001 | Noah Lott | 26-216 89 AVE. | OTTAWA | ONTARIO | L2Q 7A3 | 898954.20 |
| N005 | K Non | 499 BASELINE RD. | MONTREAL | QUEBEC | H2A 1Z6 | 452722.94 |
| P006 | Phillippa Pail | 770 87 E AVE. | OTTAWA | ONTARIO | K4P 5X9 | 432765.99 |
| P007 | Mandy Pumps | 605 FOUNTAIN ST. | OTTAWA | ONTARIO | N9M 6R5 | 800765.76 |
| P008 | Ern Payed | 57-53 GARDEN RD. | OTTAWA | ONTARIO | N2J 7W8 | 741171.15 |
| P009 | Philip Upp Garages | 6-745 MAIN ST. | TORONTO | ONTARIO | M1N 9F3 | 834789.32 |
| P010 | Ri Pent | 13 -786 RIVERSIDE DRI | OTTAWA | ONTARIO | K2D 6G4 | 913856.71 |
| R007 | A Raid | 170 60 S AVE. | OTTAWA | ONTARIO | P8Y 4L6 | 392602.69 |
| R008 | IM Right | 803 LILLICO AVE. | OTTAWA | ONTARIO | N1S 7A5 | 473958.65 |
| R010 | Ronnie Biggs | 506 CHAPEL ST. | OTTAWA | ONTARIO | L3P 9M4 | 451188.84 |
| R011 | Richmond | 124 QUEEN ST. | OTTAWA | ONTARIO | P7V 3W8 | 587962.78 |
| R020 | Round Table | 690 MAIN ST. | OTTAWA | ONTARIO | K0M 1U6 | 689593.63 |
| R025 | Ray | 989 BANK ST. | OTTAWA | ONTARIO | L3U 0Q5 | 823889.61 |
| T004 | Dick Tate | 954 27 AVE. | OTTAWA | ONTARIO | L1D 9W0 | 878625.59 |
| T005 | Honor Toze | 850 BRONSON AVE. | VANCOUVER | BC | V9M 3R3 | 455140.82 |
| T006 | Pole Tacks | 592 34 E AVE. | OTTAWA | ONTARIO | KQ7 3G2 | 506912.39 |
| T007 | Chimps Teaparty | 56-63 87 S AVE. | OTTAWA | ONTARIO | K4J 9T5 | 503117.04 |
| T008 | Joyce Tick | 822 16 S AVE. | OTTAWA | ONTARIO | P6L 1Z3 | 380542.95 |
| T009 | Tim Table | 612 RIVERSIDE DRIVE | OTTAWA | ONTARIO | K2M 4E6 | 469568.44 |
| T010 | Truckstop | 80-337 FOUNTAIN ST. | OTTAWA | ONTARIO | L1P 0V5 | 758325.88 |
| W005 | Williams Inc | 45-915 47 AVE. | OTTAWA | ONTARIO | NZ9 3V8 | 0.00 |
| W007 | The Matt Cash Co | 1596 1 N AVE. | OTTAWA | ONTARIO | N8W 3P4 | 1397587.06 |
| W007 | Witch Products | 543 28 ST. | OTTAWA | ONTARIO | L3W 0M5 | 482264.46 |
| W008 | Winchester | 40-229 89 W AVE. | OTTAWA | ONTARIO | L6C 7H4 | 480763.89 |
| W011 | Wong Way | 806 MAGNUS AVE. | TORONTO | ONTARIO | M1Q 8U5 | 442704.45 |
| W020 | Wite Wash | 824 WYNDALE CRES. | OTTAWA | ONTARIO | N3E 7B7 | 603677.11 |
| W025 | Will Scarlet | 910 BASELINE RD. | OTTAWA | ONTARIO | N2L 0T6 | 465025.32 |
| Z001 | Edward Zoff | 36-977 KING ST. | TORONTO | ONTARIO | M3A 8Z1 | 900000.00 |
